When a speech recognition system is used in a real environment, its recognition performance is affected by the surrounding noise. Most types of additional noise as well as SNRs are difficult to predict, so there is a mismatch between the training and test data. We need a method to deal with this problem. In this paper, we propose an HMM compositionbased model adaptation method with a priori noi...

Speech acts provide good insights into the communicative behavior of tweeters on Twitter. This paper is mainly concerned with speech act recognition in Twitter as a multiclass classification problem, for which we propose a set of word-based and character-based features. Inexpensive, robust and efficient, our method achieves an average F1 score of nearly 0.7 with the existence of much noise in o...

This report introduces noise robust speech recognition and proposes advanced speech analysis techniques named MSC (Modulation Spectrum Control)/DRA (Dynamic Range Adjustment). The dynamic range of cepstrum obtained from noisy speech is usually smaller than that from the same speech without noise since some speech features are hidden in noise. This difference may cause recognition errors. Theref...

In this paper, we evaluate the performance of several robust speech recognition algorithms in a noisy automobile environment as characterized by the Finnish SpeechDat–Car ASR task [1]. By applying acoustic feature compensation, model compensation, and speech detection algorithms to this task, a 51% reduction in word error rate (WER) was obtained relative to the ETSI standard ASR front–end. In a...

Automatic recognition of historical handwritten manuscripts is a daunting task due to paper degradation over time. Recognition-free retrieval or word spotting is popularly used for information retrieval and digitization of the historical handwritten documents. However, the performance of word spotting algorithms depends heavily on feature detection and representation methods. Although there exi...