2,743 research outputs found

    Band-pass filtering of the time sequences of spectral parameters for robust wireless speech recognition

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    In this paper we address the problem of automatic speech recognition when wireless speech communication systems are involved. In this context, three main sources of distortion should be considered: acoustic environment, speech coding and transmission errors. Whilst the first one has already received a lot of attention, the last two deserve further investigation in our opinion. We have found out that band-pass filtering of the recognition features improves ASR performance when distortions due to these particular communication systems are present. Furthermore, we have evaluated two alternative configurations at different bit error rates (BER) typical of these channels: band-pass filtering the LP-MFCC parameters or a modification of the RASTA-PLP using a sharper low-pass section perform consistently better than LP-MFCC and RASTA-PLP, respectively.Publicad

    Recognizing GSM Digital Speech

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    The Global System for Mobile (GSM) environment encompasses three main problems for automatic speech recognition (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

    Robust speaker recognition in the presence of speech coding distortion

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    For wireless remote access security, forensics, border control and surveillance applications, there is an emerging need for biometric speaker recognition systems to be robust to speech coding distortion. This thesis examines the robustness issue for three coders, namely, the ITU-T 6.3 kilobits per second (kbps) G.723.1, the ITU-T 8 kbps G.729 and the 12.2 kbps 3GPP GSM-AMR coder. Both speaker identiïŹcation (SI) and speaker veriïŹcation (SV) systems are considered and use a Gaussian mixture model (GMM) classiïŹer. The systems are trained on clean speech and tested on the decoded speech. To mitigate the performance loss due to mismatched training and testing conditions, four robust features, two enhancement approaches and feature (SI) and score (SV) based fusion strategies are implemented. The ïŹrst proposed novel enhancement method is feature compensation based on the afïŹne transform and is used to map the features from the test scenario to the train scenario. The second is the McCree signal enhancement approach based on the spectral envelope information. A detailed two-way analysis of variance (ANOVA) supplemented with a multiple comparison test is performed in order to show statistical significance in application of these enhancement methods

    Channel Effect Compensation in LSF Domain

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