Word error rate

About: Word error rate is a research topic. Over the lifetime, 11939 publications have been published within this topic receiving 298031 citations.

Mispronunciation Detection and Diagnosis in L2 English Speech Using Multidistribution Deep Neural Networks

Abstract: This paper investigates the use of multidistribution deep neural networks DNNs for mispronunciation detection and diagnosis MDD, to circumvent the difficulties encountered in an existing approach based on extended recognition networks ERNs. The ERNs leverage existing automatic speech recognition technology by constraining the search space via including the likely phonetic error patterns of the target words in addition to the canonical transcriptions. MDDs are achieved by comparing the recognized transcriptions with the canonical ones. Although this approach performs reasonably well, it has the following issues: 1 Learning the error patterns of the target words to generate the ERNs remains a challenging task. Phones or phone errors missing from the ERNs cannot be recognized even if we have well-trained acoustic models; and 2 acoustic models and phonological rules are trained independently, and hence, contextual information is lost. To address these issues, we propose an acoustic-graphemic-phonemic model AGPM using a multidistribution DNN, whose input features include acoustic features, as well as corresponding graphemes and canonical transcriptions encoded as binary vectors. The AGPM can implicitly model both grapheme-to-likely-pronunciation and phoneme-to-likely-pronunciation conversions, which are integrated into acoustic modeling. With the AGPM, we develop a unified MDD framework, which works much like free-phone recognition. Experiments show that our method achieves a phone error rate PER of 11.1%. The false rejection rate FRR, false acceptance rate FAR, and diagnostic error rate DER for MDD are 4.6%, 30.5%, and 13.5%, respectively. It outperforms the ERN approach using DNNs as acoustic models, whose PER, FRR, FAR, and DER are 16.8%, 11.0%, 43.6%, and 32.3%, respectively.

Speech index pruning

Abstract: A speech segment is indexed by identifying at least two alternative word sequences for the speech segment. For each word in the alternative sequences, information is placed in an entry for the word in the index. Speech units are eliminated from entries in the index based on a comparison of a probability that the word appears in the speech segment and a threshold value.

LSTM time and frequency recurrence for automatic speech recognition

Abstract: Long short-term memory (LSTM) recurrent neural networks (RNNs) have recently shown significant performance improvements over deep feed-forward neural networks (DNNs). A key aspect of these models is the use of time recurrence, combined with a gating architecture that ameliorates the vanishing gradient problem. Inspired by human spectrogram reading, in this paper we propose an extension to LSTMs that performs the recurrence in frequency as well as in time. This model first scans the frequency bands to generate a summary of the spectral information, and then uses the output layer activations as the input to a traditional time LSTM (T-LSTM). Evaluated on a Microsoft short message dictation task, the proposed model obtained a 3.6% relative word error rate reduction over the T-LSTM.

Deep neural networks for single-channel multi-talker speech recognition

Abstract: We investigate techniques based on deep neural networks (DNNs) for attacking the single-channel multi-talker speech recognition problem. Our proposed approach contains five key ingredients: a multi-style training strategy on artificially mixed speech data, a separate DNN to estimate senone posterior probabilities of the louder and softer speakers at each frame, a weighted finite-state transducer (WFST)-based two-talker decoder to jointly estimate and correlate the speaker and speech, a speaker switching penalty estimated from the energy pattern change in the mixed-speech, and a confidence based system combination strategy. Experiments on the 2006 speech separation and recognition challenge task demonstrate that our proposed DNN-based system has remarkable noise robustness to the interference of a competing speaker. The best setup of our proposed systems achieves an average word error rate (WER) of 18.8% across different SNRs and outperforms the state-of-the-art IBM superhuman system by 2.8% absolute with fewer assumptions.