On Word Error Rate Definitions and their Efficient Computation for Multi-Speaker Speech Recognition Systems

Abstract

We propose a general framework to compute the word error rate (WER) of ASR systems that process recordings containing multiple speakers at their input and that produce multiple output word sequences (MIMO). Such ASR systems are typically required, e.g., for meeting transcription. We provide an efficient implementation based on a dynamic programming search in a multi-dimensional Levenshtein distance tensor under the constraint that a reference utterance must be matched consistently with one hypothesis output. This also results in an efficient implementation of the ORC WER which previously suffered from exponential complexity. We give an overview of commonly used WER definitions for multi-speaker scenarios and show that they are specializations of the above MIMO WER tuned to particular application scenarios. We conclude with a discussion of the pros and cons of the various WER definitions and a recommendation when to use which.

Presented at ICASSP 2023

References in corpus (2)

• Graph-PIT: Generalized permutation invariant training for continuous separation of arbitrary numbers of speakers
• Speeding Up Permutation Invariant Training for Source Separation

Cited by in corpus (5)

• TS-SEP: Joint Diarization and Separation Conditioned on Estimated Speaker Embeddings
• PixIT: Joint Training of Speaker Diarization and Speech Separation from Real-world Multi-speaker Recordings
• Word Error Rate Definitions and Algorithms for Long-Form Multi-talker Speech Recognition
• Combining TF-GridNet and Mixture Encoder for Continuous Speech Separation for Meeting Transcription
• Glitch in Time: Exploiting Temporal Misalignment of IMU For Eavesdropping