Recognition of the Electrolaryngeal Speech: Comparison Between Human and Machine

Abstract

Automatic recognition of an electrolaryngeal speech is usually a hard task due to the fact that all phonemes tend to be voiced. However, using a strong language model (LM) for continuous speech recognition task, we can achieve satisfactory recognition accuracy. On the other hand, the recognition of isolated words or phrase sentences containing only several words poses a problem, as in this case, the LM does not have a chance to properly support the recognition. At the same time, the recognition of short phrases has a great practical potential. In this paper, we would like to discuss poor performance of the electrolaryngeal speech automatic speech recognition (ASR), especially for isolated words. By comparing the results achieved by humans and the ASR system, we will attempt to show that even humans are unable to distinguish the identity of the word, differing only in voicing, always correctly. We describe three experiments: the one represents blind recognition, i.e., the ability to correctly recognize an isolated word selected from a vocabulary of more than a million words. The second experiment shows results achieved when there is some additional knowledge about the task, specifically, when the recognition vocabulary is reduced only to words that actually are included in the test. And the third test evaluates the ability to distinguish two similar words (differing only in voicing) for both the human and the ASR system.