Search methods improve efficiency in speech recognition systems
A Systematic Comparison of Search Space Complexity and Efficiency for Time-Synchronous End-to-End ASR Models
Computation and Language
Summary
Automatic speech recognition systems turn spoken words into text, but how they search through possible interpretations can slow them down. The authors studied different search methods used in modern end-to-end speech recognition models, showing that fixed small searches miss important possibilities when the system is uncertain. Using a score-based method that adapts the search size dynamically helps the system work faster without losing accuracy. Their work suggests better ways to balance search complexity and speed in speech recognition.
What this means in practice
- •For speech technology engineers: Reduce latency in speech recognition by using adaptive score-based pruning rather than fixed-size beam search.
- •For mobile device developers: Integrate efficient search strategies to improve real-time speech recognition performance on resource-limited devices.
Authors
Simon Berger, Larissa Kleppel, Ralf Schlüter
Abstract
Search is a central component of Automatic Speech Recognition (ASR) systems and has been deeply investigated for classical ASR modeling based on Hidden Markov Models (HMMs). However, in work on current sequence-to-sequence ASR architectures it is often simplified to a small fixed-size beam search without external Language Model (LM). In this work, we present a comparative analysis of search space complexity and efficiency for Connectionist Temporal Classification (CTC) and neural transducer models under different pruning strategies and LM configurations. We show that these end-to-end (E2E) models work with a small search space most of the time but exhibit large temporary peaks in hypothesis count during periods of uncertainty. Score-based pruning dynamically adapts the search to model uncertainty, leading to a significantly lower Real-Time Factor (RTF). Our results highlight the limitations of search with low fixed-size beam pruning and provide practical guidelines for efficient search in E2E ASR.