Name
Abstract | ||
|---|---|---|
The Global System for Mobile (GSM) environment encompasses three main problems for automatic speech cognition (ASR) systems: noisy scenarios, source coding distortion, and transmission errors. The first one has already received much attention; however, source coding distortion and transmission errors must be explicitly addressed. In this paper, we propose an alternative front-end for speech recognition over GSM networks. This front-end is specially conceived to be effective against source coding distortion and transmission errors. Specifically, we suggest extracting the recognition feature vectors directly from the encoded speech (i.e., the bitstream) instead of decoding it and subsequently extracting the feature vectors.This approach offers two significant advantages. First, the recognition system is only affected by the quantization distortion of the spectral envelope. Thus, we are avoiding the influence of other sources of distortion as a result of the encoding-decoding process. Second, when transmission errors occur, our front-end becomes more effective since it is not affected by errors in bits allocated to the excitation signal. We have considered the half and the full-rate standard codecs and compared the proposed front-end with the conventional approach in two ASR tasks, namely, speaker-independent isolated digit recognition and speaker-independent continuous speech recognition. In general, our approach outperforms the conventional procedure, for a variety of simulated channel conditions. Furthermore, the disparity increases as the network conditions worsen. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/TSA.2005.853210 | IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING |
Keywords | Field | DocType |
coding distortion, Global System for Mobile (GSM) networks, speech coding, speech recognition, tandeming, transmission errors, wireless networks | Speech processing,GSM,Feature vector,Speech coding,Pattern recognition,Computer science,Voice activity detection,Speech recognition,Feature extraction,Speaker recognition,Artificial intelligence,Distortion | Journal |
Volume | Issue | ISSN |
13 | 6 | 1063-6676 |
Citations | PageRank | References |
8 | 0.54 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| j maciasguarasa | 1 | 92 | 19.30 |
| Carmen Peláez-moreno | 2 | 130 | 22.07 |
| Fernando Díaz-de-María | 3 | 201 | 32.14 |