What is the role of tokenization in speech recognition?

What is the role of tokenization in speech recognition? Tokenization in speech recognition involves breaking down raw audio input into smaller, manageable units (tokens) that a machine learning model can process. Unlike text tokenization, which splits words or subwords, speech tokenization often works with acoustic features like spectrogram frames or phonetic segments. For example, a model might split audio into 10-20 millisecond frames, each representing a time step with frequency information. These tokens act as the input features for neural networks, enabling the model to learn patterns in speech, such as phonemes or word boundaries. Without tokenization, processing continuous audio signals directly would be computationally impractical and less effective for training.

Examples and Practical Impact A common approach is using frame-level tokenization with techniques like Connectionist Temporal Classification (CTC). Here, each audio frame is treated as a token, and the model predicts phonemes or characters for each frame before aggregating them into words. For instance, in a system transcribing English, a 10ms frame might correspond to part of a phoneme like “sh” or “ah.” Subword tokenization methods, such as Byte-Pair Encoding (BPE), are also used post-acoustic processing to split text outputs into units like “un-" and "-able,” which helps handle rare words. This step-by-step tokenization allows models to align audio with text accurately, reducing errors in transcribing accents or fast speech.

Challenges and Techniques Tokenizing speech poses unique challenges. Audio signals vary in speed, tone, and background noise, making it hard to define consistent token boundaries. For example, a rapid “howareyou” might blur word separations. To address this, hybrid models combine frame-level tokenization with language models that predict likely word sequences. Adaptive methods, like dynamically adjusting frame lengths or using pretrained tokenizers (e.g., Wav2Vec 2.0’s learned speech units), improve robustness. The choice of token size—too small (noisy) or too large (loss of detail)—also affects accuracy. By balancing these factors, tokenization ensures efficient, scalable speech recognition systems while maintaining transcription quality.