Speech recognition requires several types of data to function effectively, including:
Audio Data – The core input, which includes spoken words, phrases, or sentences in various formats (e.g., WAV, MP3). This data must cover diverse accents, speaking speeds, and background conditions.
Example: A dataset of people reading news articles in different languages and dialects.
Transcripts – Text versions of the audio data, used to train the model to map sounds to words. These must be accurately aligned with the audio.
Example: A dataset where each audio clip has a corresponding text file with the exact spoken content.
Linguistic Data – Information about grammar, syntax, and language models to help the system predict likely word sequences.
Example: A large corpus of text (e.g., books, articles) to train statistical language models.
Acoustic Data – Features extracted from audio, such as pitch, tone, and frequency, which help the model distinguish between similar-sounding words.
Example: Mel-Frequency Cepstral Coefficients (MFCCs) derived from audio signals.
Noise and Variability Data – Recordings in different environments (e.g., noisy streets, quiet rooms) to improve robustness.
Example: A dataset with the same spoken commands recorded in a quiet office and a crowded café.
For speech recognition tasks, Tencent Cloud offers Intelligent Speech Recognition (ISR), which supports multi-language, high-accuracy transcription and can be integrated with other AI services for enhanced performance.