CN115346512A - Multi-emotion voice synthesis method based on digital people - Google Patents
Multi-emotion voice synthesis method based on digital people Download PDFInfo
- Publication number
- CN115346512A CN115346512A CN202211002427.6A CN202211002427A CN115346512A CN 115346512 A CN115346512 A CN 115346512A CN 202211002427 A CN202211002427 A CN 202211002427A CN 115346512 A CN115346512 A CN 115346512A
- Authority
- CN
- China
- Prior art keywords
- phoneme
- audio
- model
- timestamp
- emotion
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000001308 synthesis method Methods 0.000 title claims abstract description 11
- 230000008451 emotion Effects 0.000 claims abstract description 44
- 238000012549 training Methods 0.000 claims abstract description 34
- 238000000034 method Methods 0.000 claims abstract description 19
- 239000013598 vector Substances 0.000 claims description 21
- 238000001228 spectrum Methods 0.000 claims description 11
- 230000015572 biosynthetic process Effects 0.000 abstract description 11
- 238000003786 synthesis reaction Methods 0.000 abstract description 11
- 230000033764 rhythmic process Effects 0.000 abstract description 5
- 230000000694 effects Effects 0.000 abstract description 4
- 230000006870 function Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000003993 interaction Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 230000007935 neutral effect Effects 0.000 description 3
- 230000002996 emotional effect Effects 0.000 description 2
- 238000002372 labelling Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000035807 sensation Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 238000013136 deep learning model Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000036651 mood Effects 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L13/00—Speech synthesis; Text to speech systems
- G10L13/08—Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L15/00—Speech recognition
- G10L15/26—Speech to text systems
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
- G10L25/24—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being the cepstrum
-
- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
- G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
- G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
- G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
- G10L25/63—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination for estimating an emotional state
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Acoustics & Sound (AREA)
- Audiology, Speech & Language Pathology (AREA)
- Human Computer Interaction (AREA)
- Physics & Mathematics (AREA)
- Computational Linguistics (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Child & Adolescent Psychology (AREA)
- General Health & Medical Sciences (AREA)
- Hospice & Palliative Care (AREA)
- Psychiatry (AREA)
- Machine Translation (AREA)
Abstract
The invention discloses a multi-emotion voice synthesis method based on digital people, which comprises the following steps: acquiring audio corpora under various emotions; extracting text information and phoneme timestamps in the audio corpus to construct a first training data set; training a pre-constructed phoneme timestamp prediction model through the first training data set; predicting the phoneme stamps and the pronunciation frame lengths of all pronunciation audios in the text information through the trained phoneme timestamp prediction model to generate a phoneme sequence; inputting the phoneme sequence into a pre-trained acoustic model to obtain a synthetic audio; the method can avoid manual addition of rhythm labels and improve the model training efficiency; through the learning of the audio time domain and frequency domain characteristics, a more vivid audio synthesis effect is realized.
Description
Technical Field
The invention relates to the technical field of virtual digital people, in particular to a multi-emotion voice synthesis method based on digital people.
Background
At present, although the traditional voice synthesis sound can eliminate mechanical sound, so that people can hardly distinguish real person from synthesized sound, the same text only has single emotion. For speech synthesis of different emotions, different audio corpora are often used to respectively train and obtain audios corresponding to the emotions through different models. Not only does this require a large amount of audio corpus, but the switching between different emotions also presents a mechanical sensation that is difficult to satisfy in highly anthropomorphic speech interactions such as digital human interaction. In the multi-emotion speech synthesis, although there is no hard index such as the error rate, similar problems are encountered as in the speech task such as speech recognition. Although speech synthesis can synthesize a text requiring synthesized speech into a corresponding audio, its emotional richness is not as high as that of a human being. This is especially important in digital human voice interaction.
Common deep learning models, such as tacontron, are based on statistical models to realize speech synthesis, often a speech synthesis system in which one model can only train one emotion is required, and switching between different emotion models is required to realize multi-emotion expression. This not only requires training with a large corpus, but also switching back and forth between different speech models can create a severe mechanical sensation. Furthermore, sounds synthesized by different emotion speech models cannot model emotions continuously, i.e., the expressed emotions are not the same, and there is no intermediate soft emotion expression. Different emotion audio corpora are directly added with emotion labels and are put into a model for training, so that synthesized voice can not accord with rhythm characteristics of normal expression emotion of people, namely words which should be accentuated can not be caught, and the voice is very strange to sound.
Adding prosodic tags to the corpus can alleviate the problems, but tagging the corpus is a tedious process, and a good result can be obtained only by a large amount of manual examination. Moreover, in the actual voice interaction, prosody labeling is also needed for the text to be synthesized, the system becomes complicated, and the labeling pair is not likely to affect the final synthesis effect.
Therefore, how to provide a multi-emotion voice synthesis method based on digital people is a problem that needs to be solved urgently by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a multi-emotion voice synthesis method based on digital people, which can avoid manual addition of rhythm labels and improve the model training efficiency; through the learning of the audio time domain and frequency domain characteristics, a more vivid audio synthesis effect is realized.
In order to achieve the purpose, the invention adopts the following technical scheme:
a multi-emotion voice synthesis method based on digital people comprises the following steps:
acquiring audio corpora under various emotions;
extracting text information and phoneme timestamp labels in the audio corpus to construct a training data set;
carrying out supervised training on a pre-constructed timestamp prediction model through the training data set;
predicting text information to be synthesized through the trained timestamp prediction model and a preset emotion vector to obtain a phoneme timestamp;
and inputting the phoneme timestamp into a trained acoustic model to obtain a synthetic audio.
Further, in the audio corpus under the plurality of emotions, the emotion includes one or more of neutral, happy, sad, angry, surprised and fear.
Further, extracting text information and phoneme timestamp labels in the audio corpus, and constructing a first training data set includes:
clipping the audio corpus into a plurality of audios,
performing voice recognition on the audio through an ASR voice recognition model to obtain text information; converting the text information into text pinyin, and decomposing the text pinyin into a plurality of phonemes according to a phoneme dictionary to form a phoneme sequence;
performing phoneme alignment on the phoneme sequence to obtain a phoneme timestamp label of each audio;
and inserting a pause frame length into the phoneme sequence and splicing phoneme time stamp labels corresponding to the audio to generate the training data set.
Further, the phoneme timestamp prediction model is a Bilstm model, and a loss function of the Bilstm model adopts a mean square error loss function.
Further, the acoustic model comprises a synthesizer and a vocoder;
generating a model embedding vector according to the phoneme timestamp;
fitting the model embedded vector through the synthesizer to obtain mel frequency spectrum characteristics;
and obtaining the synthesized audio by the voice coder according to the mel frequency spectrum characteristic input value.
Further, generating a model embedding vector according to the phoneme timestamp, comprising the steps of:
and respectively calculating the length difference of a pronunciation frame between each phoneme and adjacent pinyin according to the phoneme timestamp, and splicing the length difference of the pronunciation frame into the phoneme timestamp label to generate a model embedded vector.
Further, the vocoder is a pre-trained hifi-gan model.
Further, the mel frequency spectrum characteristic channel is 80, the hop_size is 256, and the win_size is 1024.
Furthermore, after extracting the text information of the audio information, the invention converts the special characters needing to be converted into Chinese through regularization processing to obtain the regularization standard text, and has the advantages that:
according to the technical scheme, compared with the prior art, the multi-emotion voice synthesis method based on the digital human is disclosed, emotion vectors are combined with timestamp information, the voice emotion characteristics are learned by utilizing a deep learning technology, errors possibly caused by a rhythm label step are reduced, a model can pronounce more naturally, emotion is more vivid, a rhythm prediction step is omitted, speed is increased, and instantaneity is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the provided drawings without creative efforts.
FIG. 1 is a schematic diagram of a digital human-based multi-emotion speech synthesis method according to the present invention;
FIG. 2 is a schematic diagram of a method for extracting phoneme time stamp tags according to the present invention;
FIG. 3 is a schematic diagram of a training data set construction method according to the present invention;
FIG. 4 is a schematic diagram of an acoustic model training method according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the embodiment of the invention discloses a multi-emotion voice synthesis method based on digital people, comprising the following steps:
s1: acquiring audio corpora under various emotions;
in one embodiment, in the audio corpus under a plurality of emotions, the emotion may include one or more of neutral, happy, sad, angry, surprised, and fear. And moreover, the volumes of the audio corpora are consistent, and the moods are consistent.
The method comprises the following specific steps:
a professional broadcaster records multi-emotion audio corpus in a professional recording studio; the broadcaster needs to record six kinds of emotion audio corpora, including: the music is neutral, happy, sad, angry, surprised and frightened, and each emotion records about ten thousand of audios. The broadcaster records various emotion audios respectively, and should keep the same volume as much as possible, and the speaking speed and the tone of the same emotion are kept consistent as much as possible.
In order to ensure the recording efficiency, the recording is not suspended in the process of recording the audio once, and the sound emitted by a speaker in a pause or non-speech mode needs to be edited subsequently in the recording process of the speaker. After the audio recorded in different batches is edited, the volume of each audio needs to be normalized, so that the quality of the training audio is ensured.
S2: extracting text information and phoneme timestamps in the audio corpus to construct a training data set;
in one embodiment, the text information is converted into text pinyin, and the text pinyin is decomposed into a plurality of phonemes according to a phoneme dictionary to form a phoneme sequence;
performing phoneme alignment on the phoneme sequence to obtain a phoneme timestamp label of each audio;
and inserting pause frame length into the phoneme sequence and splicing phoneme time stamp labels corresponding to the audios to generate a training data set. Where the pause frame length may be derived from a factor timestamp label, the phoneme timestamp contains the phonemes and corresponding durations, one for the phonemes and one for the time, where splicing refers to copying the phonemes into multiples of their time frame length. For example, the phoneme abc, the time frame lengths of the three phonemes are 123 respectively, and then the phoneme copy frame length is abbcc.
The method comprises the following specific steps:
as shown in fig. 2, the announcer records the audio and processes the audio clip to obtain a strip of training audio corpus. In order to prevent errors such as word missing, word multiple and the like of a broadcaster during audio recording, firstly, voice recognition processing is carried out on each audio corpus to obtain text content of each audio. And obtaining correct audio and corresponding text information after manual verification. After the correct text information is obtained, the text content is converted into pinyin, and the pinyin phoneme dictionary are obtained through sorting. The audio file and the corresponding pinyin file are sorted, and phoneme alignment is carried out by using an acoustic model through a phoneme dictionary, such as: an MFA tool. After the phoneme alignment, phoneme timestamp information of each audio can be obtained. And (4) organizing the text, the phoneme timestamp, the audio file and other information of each audio corpus to serve as a training data text for the next step of training the model.
S3: carrying out supervised training on a pre-constructed phoneme timestamp prediction model through the first training data set;
as shown in fig. 3, the phoneme timestamp prediction model is trained based on the text information and the phoneme timestamp information in the first training data set.
The prediction time stamp information is used to predict phoneme time stamp information corresponding to an audio sound by a model after a text is input when speech is synthesized, and to know which phoneme sound should be uttered at a certain time when audio synthesis is performed. The unit of the time stamp is expressed in frames, and the time of sounding one phoneme may take several frames. The phoneme time stamp prediction model inputs that only text information is contained, but discontinuous pause exists in actual audio pronunciation, in order to represent the discontinuous pause in an output prediction structure, an sp symbol is added behind each character pinyin to represent the pause frame length, and if the sp has no pause frame length, the corresponding label is 0. The prediction model structure adopts a Bilstm model, and the loss function adopts a mean square error loss function. After text pinyin is input, a phoneme dictionary of the pinyin is inquired and converted into a phoneme sequence containing sp, the phoneme sequence is spliced with a corresponding emotion label vector after being embedding and then input into a Bilstm network, and the corresponding label is the frame length of pronunciation of each phoneme. And training until the model converges.
S4: predicting text information to be synthesized through the trained timestamp prediction model and a preset emotion vector to obtain a phoneme timestamp;
s5: and inputting the phoneme timestamp into a trained acoustic model to obtain a synthetic audio.
In one embodiment, after training the phoneme timestamp prediction model, the pronunciation content within each frame length of the pronunciation audio can be obtained by inputting the text.
The audio content is then generated by an acoustic model.
The input is a phoneme sequence, each phoneme unit consists of a frame length phoneme corresponding to the phoneme unit, and the phoneme sequence is spliced with an emotion vector after being embedding.
In order to grasp the acoustic features of multi-emotion audio emotions, the acoustic model needs to find the phoneme where the accents are located.
Different audio emotional characteristics are embodied on a time domain and a frequency domain, and phoneme time stamp information is obtained, so that the time domain information of a phoneme where stress is located can be learned by a model by utilizing the frame length information of the phoneme.
The current frame length of each character, the frame length difference relative to the previous character and the frame length difference of the next character are spliced into an embedding vector of the previous phoneme sequence and then input into an acoustic model, so that pronunciation audios with different emotions can be learned.
As shown in fig. 4, the acoustic model consists essentially of two parts, a synthesizer and a vocoder. The mel frequency spectrum feature extracted by the audio frequency expectation is used as an output label of the synthesizer, a loss function is calculated according to the mel frequency spectrum output by the synthesizer and the output label, and model training is carried out to judge the convergence of the synthesizer; the mel channel is 80, the hop _sizeis 256, and the win _sizeis 1024.
The vocoder adopts a hifi-gan model, and the vocoder adopts a pre-training model, namely pre-training is performed on large-scale data in advance.
And training a synthesizer model, fitting corresponding audio mel frequency spectrum characteristics by inputting an embedding vector of a phoneme sequence, and finally inputting the generated mel frequency spectrum characteristics into a previously pre-trained hifi-gan model to generate audio so as to test the model effect.
In this embodiment, when the trained model is actually applied, only the text and the emotion to be expressed need to be acquired, and no prosody label needs to be input for the text.
After inputting text and emotion labels, special characters required to be converted into Chinese are first converted into Chinese by regularization, such as Arabic numerals, telephone numbers, unit names, operation symbols and the like.
And converting the processed sentence into a corresponding phoneme label in a phoneme dictionary mode, inserting an sp symbol into a phoneme sequence, splicing an emotion vector, and inputting the emotion vector into a timestamp prediction model. And after the time stamp of each phoneme is obtained, calculating the time stamp information and combining the time stamp information with the previously input emotion phoneme vector to obtain an expanded emotion phoneme vector with the phoneme time stamp information, and inputting the expanded emotion phoneme vector into a synthesizer model to obtain the output mel frequency spectrum feature. And inputting the audio data into the hifi-gan model so as to generate an audio file corresponding to the text and complete the synthesis of the multi-emotion voice.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed in the embodiment corresponds to the method disclosed in the embodiment, so that the description is simple, and the relevant points can be referred to the description of the method part.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (9)
1. A multi-emotion voice synthesis method based on digital people is characterized by comprising the following steps:
acquiring audio corpora under various emotions;
extracting text information and phoneme timestamp labels in the audio corpus to construct a training data set;
carrying out supervised training on a pre-constructed timestamp prediction model through the training data set;
predicting text information to be synthesized through the trained timestamp prediction model and a preset emotion vector to obtain a phoneme timestamp;
and inputting the phoneme timestamp into a trained acoustic model to obtain a synthetic audio.
2. The method of claim 1, wherein the emotion comprises one or more of neutrality, happiness, sadness, anger, surprise and fear in the audio corpus under emotion.
3. The method as claimed in claim 1, wherein the step of extracting text information and phoneme timestamp labels in the audio corpus and constructing the training data set comprises:
clipping the audio corpus into a plurality of audios,
performing voice recognition on the audio through an ASR voice recognition model to obtain text information; converting the text information into text pinyin, and decomposing the text pinyin into a plurality of phonemes according to a phoneme dictionary to form a phoneme sequence;
performing phoneme alignment on the phoneme sequence to obtain a phoneme time stamp label of each audio;
and inserting a pause frame length into the phoneme sequence and splicing phoneme time stamp labels corresponding to the audio to generate the training data set.
4. The method as claimed in claim 1, wherein the phoneme timestamp prediction model is a Bilstm model, and the loss function of the Bilstm model is a mean square error loss function.
5. The method of claim 1, wherein the acoustic model comprises a synthesizer and a vocoder;
generating a model embedding vector according to the phoneme timestamp;
fitting the model embedded vector through the synthesizer to obtain mel frequency spectrum characteristics;
and obtaining the synthesized audio by the voice coder according to the mel frequency spectrum characteristic input value.
6. The method of claim 5, wherein model-embedded vectors are generated based on the phoneme time stamps, and the steps of:
and respectively calculating the length difference of a pronunciation frame between each phoneme and adjacent pinyin according to the phoneme timestamp, and splicing the length difference of the pronunciation frame into the phoneme timestamp label to generate a model embedded vector.
7. The method as claimed in claim 5, wherein the vocoder is a pre-trained hifi-gan model.
8. The method of claim 5, wherein the mel frequency spectrum feature has a channel of 80, hop size of 256 and win size of 1024.
9. The method according to claim 1, wherein the regularization canonical text is obtained by converting a special character to be converted into chinese through regularization after extracting text information of the audio information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211002427.6A CN115346512A (en) | 2022-08-24 | 2022-08-24 | Multi-emotion voice synthesis method based on digital people |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211002427.6A CN115346512A (en) | 2022-08-24 | 2022-08-24 | Multi-emotion voice synthesis method based on digital people |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115346512A true CN115346512A (en) | 2022-11-15 |
Family
ID=83954447
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211002427.6A Pending CN115346512A (en) | 2022-08-24 | 2022-08-24 | Multi-emotion voice synthesis method based on digital people |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115346512A (en) |
-
2022
- 2022-08-24 CN CN202211002427.6A patent/CN115346512A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108899009B (en) | Chinese speech synthesis system based on phoneme | |
| US7280968B2 (en) | Synthetically generated speech responses including prosodic characteristics of speech inputs | |
| CN112184858B (en) | Virtual object animation generation method and device based on text, storage medium and terminal | |
| JP2018146803A (en) | Voice synthesizer and program | |
| CN110390928B (en) | Method and system for training speech synthesis model of automatic expansion corpus | |
| JP2020034883A (en) | Voice synthesizer and program | |
| CN116092472A (en) | Speech synthesis method and synthesis system | |
| CN115101046A (en) | Method and device for synthesizing voice of specific speaker | |
| Bettayeb et al. | Speech synthesis system for the holy quran recitation. | |
| JP2016151736A (en) | Speech processing device and program | |
| CN116129868A (en) | Method and system for generating structured photo | |
| CN114708848A (en) | Method and device for acquiring size of audio and video file | |
| CN115359775A (en) | End-to-end tone and emotion migration Chinese voice cloning method | |
| CN114242032A (en) | Speech synthesis method, apparatus, device, storage medium and program product | |
| CN115346512A (en) | Multi-emotion voice synthesis method based on digital people | |
| JPH08335096A (en) | Text voice synthesizer | |
| CN113628609A (en) | Automatic audio content generation | |
| Takaki et al. | Overview of NIT HMM-based speech synthesis system for Blizzard Challenge 2012 | |
| JP2021148942A (en) | Voice quality conversion system and voice quality conversion method | |
| Al-Said et al. | An Arabic text-to-speech system based on artificial neural networks | |
| CN115547296B (en) | Voice synthesis method and device, electronic equipment and storage medium | |
| Dessai et al. | Development of Konkani TTS system using concatenative synthesis | |
| CN113178186B (en) | Dialect voice synthesis method and device, electronic equipment and storage medium | |
| Lu et al. | Unlocking the Potential: an evaluation of Text-to-Speech Models for the Bahnar Language | |
| Cai et al. | The DKU Speech Synthesis System for 2019 Blizzard Challenge |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Country or region after: China Address after: Room 911, 9th Floor, Block B, Xingdi Center, Building 2, No.10, Jiuxianqiao North Road, Jiangtai Township, Chaoyang District, Beijing, 100000 Applicant after: Beijing Zhongke Shenzhi Technology Co.,Ltd. Address before: 100000 room 311a, floor 3, building 4, courtyard 4, Yongchang Middle Road, Beijing Economic and Technological Development Zone, Daxing District, Beijing Applicant before: Beijing Zhongke Shenzhi Technology Co.,Ltd. Country or region before: China |