Speech Dataset Project: A Comprehensive Overview
The Speech Dataset Project is a remarkable initiative that seeks to provide a wide range of resources for speech recognition, synthesis, and speaker recognition across multiple languages. These datasets are crucial for developing and improving modern speech technologies, enabling machines to understand and generate human speech more effectively. Here's an in-depth look at what this project offers:
Speech Recognition Datasets
Speech recognition is the process of converting spoken language into text. It's a fundamental component of technologies like virtual assistants and automated transcription services. The Speech Dataset Project provides extensive datasets for speech recognition in various languages:
Chinese Datasets
- WenetSpeech: At 10,000 hours, this dataset offers a vast resource for speech recognition in Chinese.
- KeSpeech: With 1542 hours of data, it supports speech recognition, speaker verification, subdialect identification, and voice conversion.
- Aishell2 and Aishell: These datasets provide 1000 and 150 hours, respectively, ideal for training robust speech recognition models.
English Datasets
- LibriSpeech: This well-known dataset consists of 960 hours of read English speech.
- Common Voice: An extensive dataset with 2015 hours of English speech from a range of contributors to ensure diversity in speech patterns.
Multilingual Datasets
- Multilingual LibriSpeech: With a staggering 44,659 hours, this dataset is invaluable for multilingual speech recognition research.
Speech Synthesis Datasets
Speech synthesis, or text-to-speech (TTS), is the process by which text is converted into spoken voice output. The project's datasets for speech synthesis allow developers to create more natural and human-like speech from text.
Chinese Synthesis Datasets
- Aishell3: With 85 hours of speech data, this dataset is perfect for developing Chinese TTS systems.
- Opencpop: A unique dataset designed for singing voice synthesis.
English Synthesis Datasets
- LibriTTS: Containing 585 hours of speech, this extensive dataset supports high-quality TTS model development.
- Hi-Fi Multi-Speaker English TTS Dataset: Comprising 291.6 hours, this dataset provides diverse speaker representation.
Datasets for Speech Recognition & Speaker Diarization
Speaker diarization separates speakers in an audio stream, essential for applications like conference transcription. The datasets in this category support both speech recognition and diarization:
- Aishell4: This 120-hour dataset simulates real-world conference scenarios with 8-channel data, crucial for both diarization and recognition.
- CHiME-6: Available for English, this dataset is specifically tailored to challenging acoustic environments like everyday environments.
Speaker Recognition Datasets
Speaker recognition technology identifies or verifies a person based on their voice. The datasets for speaker recognition provide essential data for developing systems that can distinguish between different speakers:
Chinese Datasets
- CN-Celeb: A comprehensive resource for speaker recognition research in Chinese.
- KeSpeech: As mentioned earlier, covers multiple applications including speaker recognition.
English Datasets
- VoxCeleb Data: Widely used in the speaker recognition community, this dataset includes a wide variety of speakers and scenarios.
Additional Resources
The project also provides datasets for non-speech sounds, which are crucial for the development of ambient sound recognition technologies:
- MUSAN: A diverse collection of music, speech, and noise recordings.
- AudioSet: A large ontology of human and environmental sounds.
In conclusion, the Speech Dataset Project is an invaluable resource for researchers and developers who are working to advance speech technologies. By providing access to a wide variety of speech data across numerous languages and applications, the project supports the development of more capable and sophisticated speech recognition, synthesis, and speaker recognition systems.