Neural representations of words are thought to have a complex spatio-temporal cortical basis. It has been suggested that spoken word recognition is not a process of feed-forward computations from phonetic to lexical forms, but rather involves the online integration of bottom-up input with stored lexical knowledge. Using direct neural recordings from the temporal lobe, we examined cortical respo...

As to traditional n-gram model, smaller n value is an inherent defect for estimating language probabilities in speech recognition, simply because that estimation could not be executed over farther word association but by means of short sequential word correlated information. This has an strong effect on the performance of speech recognition. This paper introduces an integrated language modeling...

Perplexity is a widely used measure to evaluate word prediction power of a word-based language model. It can be computed independently and has shown good correlation with word error rate (WER) in speech recognition. However, for character based languages, character error rate (CER) is commonly used instead of WER as the measure for speech recognition, although language model is still word based...

State-of-the-art speech recognition systems rely heavily on three basic components: an acoustic model, a pronunciation lexicon and a language model. To build these components, a researcher needs linguistic as well as technical expertise, which is a barrier in lowresource domains. Techniques to construct these three components without having expert domain knowledge are in great demand. Urdu, des...

This paper proposes to apply machine learning techniques to the task of combining outputs of multiple LVCSR models, where, as features of machine learning, information such as the models which output the hypothesized word, its part-of-speech, and its syllable length are useful for improving the word recognition rate. Experimental results show that the combination result outperforms several base...

Does prosody help word recognition? In this paper, we propose a novel probabilistic framework in which word and phoneme are dependent on prosody in a way that improves word recognition. The prosody attribute that we investigate in this study is the duration lengthening effects of the speech segments in the vicinity of intonational phrase boundaries. Explicit Duration Hidden Markov Model (EDHMM)...