The definition and classification of TTS annotation

TTS annotation refers to the annotation work performed during the text-to-speech synthesis process. TTS technology refers to the technology that automatically converts text into speech. It has a wide range of applications, including voice assistants, voice navigation, automatic voice response systems, etc.

The types of TTS annotation include the following:

Text annotation: original text, including speech recognition transliteration and natural language generation text.

Phoneme annotation: Mark the position of each phoneme in the text and the corresponding phoneme content, which is used to train the phoneme classifier in the TTS model.

Prosodic annotation refers to annotating basic phonetic units (such as syllables or words) in text and recording their phonetic attributes, such as pitch, duration, and intensity. These annotations are used to train prosody models in text-to-speech (TTS) models.

Voice annotation: Annotate the basic information of the speech audio generated by TTS, such as audio length, sampling rate, bit depth, etc.

Intention annotation: Annotate the intention or emotional information in the text, which is used to train the emotion model in the TTS model or the emotion recognition model in voice interaction.

Pronunciation annotation: Marks the pronunciation differences in different languages ​​or dialects and is used to train the pronunciation model in the TTS model.

Speech speed annotation: Mark the speech speed information of the text, including sentence pauses, intonation, speech speed changes, etc., used to train the speech speed control model in the TTS model.

Speech synthesis parameter labeling: label the characteristic parameters in the TTS model, such as fundamental frequency, harmonics, vocal tract parameters, etc., which are used to train the speech synthesis model in the TTS model.

The purpose of TTS annotation is to enable computers to correctly understand and process text, and then generate natural and smooth speech. When performing TTS annotation, the text needs to be processed such as word segmentation, phoneme conversion, and syllable division, so that the computer can accurately understand the meaning and pronunciation rules of each word, each phoneme, and each syllable. The result of TTS annotation is an annotation file containing information such as phonemes, syllables, stress and rhythm.

When performing TTS annotation, you need to pay attention to some key issues. First, the text needs to be segmented, dividing long sentences into phrases or words, so that the computer can correctly understand the meaning and grammatical structure of each word. Secondly, phoneme conversion needs to be performed to convert each word into the corresponding phoneme sequence. Phoneme is the smallest phoneme that constitutes language and the basic unit of speech synthesis. When converting phonemes, it is necessary to consider the rules of continuous reading and diacritics between phonemes to ensure that the generated speech is smooth and natural.

In addition to word segmentation and phoneme conversion, TTS annotation also requires syllable division, stress marking, and rhyme marking. Syllables are the combination of phonemes that make up a word, and each syllable has a stress. When performing TTS annotation, the stress position of each word needs to be marked to ensure that the generated speech has the correct stress and rhythm. At the same time, prosodic information, such as intonation, speaking speed, pauses, etc., also needs to be annotated to make the generated speech more natural and smooth.

TTS annotation usually has two methods, one is manual annotation and the other is AI annotation.

Manual annotation is a manual annotation process that requires human annotators to listen to the text word by word and convert it into corresponding speech annotations. AI annotation uses artificial intelligence algorithms to automatically convert text into voice annotations, thereby reducing the cost and time of manual annotation. Although AI annotation is faster and more efficient, it may not be as good as human annotation in quality because the AI ​​algorithm may make errors or fail to recognize specific speech features. Therefore, in practical applications, it is usually necessary to combine the two annotation methods to improve the quality and efficiency of annotation.

You can learn about NetEase Fuxi's crowdsourcing data service, using the platform to build an RLHF training strategy, allowing manual annotators to participate in the model training and tuning process in real time. The platform will screen typical feature data for manual annotation first, and reflow model training in real time based on manual annotation results to form a data closed loop, improve model effects, and achieve automatic annotation. Finally, the platform will also calculate the user's historical task performance in real time based on the user's historical task results, and perform automatic quality inspection on all data.

In general, TTS annotation refers to the work that requires annotating speech data in TTS technology, aiming to enable computers to correctly understand and process text, and then generate natural and smooth text. voice. TTS annotation requires word segmentation, phoneme conversion, syllable division, stress marking, and rhyme annotation, etc., and usually requires manual annotation or automated annotation.

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