Name
Abstract | ||
|---|---|---|
Today, the performance of even the best state-of-the-art Automatic Speech Recognition (ASR) tends to deteriorate obviously when speech is transmitted over telephone lines. How to improve ASR robustness in noisy channel environments becomes a life and death problem for many real applications. The challenge in addressing such network environments is that they change every moment and show quite different characteristics in terms of signal-to-noise ratio (SNR), stationarity and spectral structure. Previous adaptation methods with complex parameterization could not follow these channel-related variations reliably during the process of a single utterance. So an online adaptation especially designed for noisy channel environments is necessary. In this paper, a prototype library is established to describe acoustic similarities by exploring large amount of channel-contaminated data. The pre-calculated statistics of this library makes it possible to implement a fast channel selection reliably. Furthermore, a Bayesian learning scheme is developed to compensate channel distortion dynamically through a linear interpolation across the library. In our experiments, the new method leads to 10% relative reduction in Word Error Rate (WER) with respect to conventional Maximum Likelihood Linear Regression (MLLR). |
Year | Venue | Keywords |
|---|---|---|
2006 | SPPRA | online adaptation,prototype library,acoustic similarity,asr robustness,channel distortion dynamically,cepstrum interpolation,channel-contaminated data,robust speech recognition,word error rate,previous adaptation method,fast channel selection,noisy channel environment,automatic speech recognition |
Field | DocType | ISBN |
Bayesian inference,Pattern recognition,Computer science,Word error rate,Cepstrum,Interpolation,Communication channel,Robustness (computer science),Speech recognition,Artificial intelligence,Linear interpolation,Distortion | Conference | 0-88986-580-9 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huayun Zhang | 1 | 1 | 3.41 |
| Jun Xu | 2 | 19 | 1.81 |