We describe a novel method for tuning the decoding parameters of a speech-to-text system so as to minimize word error rate (WER) subject to an over-all time constraint. When applied to three sub-realtime systems for recognizing English conversational telephone speech, the method gave speed improvements of up to 21.1% while at the same time reducing WER by up to 6.7%.

This project compares the accuracy of two automatic speech recognition (ASR) systems–Bing Speech and YouTube’s automatic captions–across gender, race and four dialects of American English. The dialects included were chosen for their acoustic dissimilarity. Bing Speech had differences in word error rate (WER) between dialects and ethnicities, but they were not statistically reliable. YouTube’s a...

Debates in the European Parliament are simultaneously translated into the official languages of the Union. These interpretations are broadcast live via satellite on separate audio channels. After several months, the parliamentary proceedings are published as final text editions (FTE). FTEs are formatted for an easy readability and can differ significantly from the original speeches and the live...

Diversity is crucial to reducing the word error rate (WER) when fusing multiple automatic speech recognition (ASR) systems. We present an empirical analysis linking diversity and fusion performance. We transcribed speech from the first 2012 US Presidential debate using multiple ASR systems trained with the Kaldi toolkit. We used the N-best ROVER algorithm to perform hypothesis fusion and measur...

This paper focuses on an approach to improve automatic phonetic transcription of proper nouns. The method is based on a two-level iterative process that extract the phonetic variants from the audio signals before filtering the irrelevant variants. The evaluation of the method shows a decreasing of the Word Error Rate (WER) on segments of speech with proper nouns, without affecting negatively th...

Hidden Markov Models (HMMs) are known to model the duration of sound units poorly. In this paper we present a technique to normalize the duration of each phone to overcome this weakness, with the conjecture that speech with normalized phone durations may be better modeled and discriminated using standard HMM acoustic models. Duration normalization is accomplished by dropping frames if a phone i...