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

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

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

Recognizing Voice Over IP: A Robust Front-End for Speech Recognition on the World Wide Web

The Internet Protocol (IP) environment poses two relevant sources of distortion to the speech recognition problem: lossy speech coding and packet loss. In this paper, we propose a new front-end for speech recognition over IP networks. Specifically, we suggest extracting the recognition feature vectors directly from the encoded speech (i.e., the bit stream) instead of decoding it and subsequently extracting the feature vectors. This approach offers two significant benefits. 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 due to the encoding-decoding process. Second, when packet loss occurs, our front-end becomes more effective since it is not constrained to the error handling mechanism of the codec. We have considered the ITU G.723.1 standard codec, which is one of the most preponderant coding algorithms in voice over IP (VoIP) and compared the proposed front-end with the conventional approach in two automatic speech recognition (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 packet loss rates. Furthermore, the improvement is higher as network conditions worsen.Publicad

Subjective tests of speaker recognition for selected voice disguise techniques

Research work on the effectiveness of voice disguise techniques is important for the development of biometric systems (surveillance) as well as phonoscopic research (forensics). A speaker recognition system or a listener can be deliberately or non-deliberately misled by technical or natural methods. It is important to determine the impact of these techniques on both automatic systems and live listeners. This paper presents the results of listening tests conducted on a group of 40 people. The effectiveness of speaker recognition was investigated using selected natural (chosen from four groups of deliberate natural techniques: phonation, phonemic, prosodic and deformation) and technical (pitch shifting, GSM coding) voice disguise techniques. The results were related to the previously obtained outcomes for the automatic method of verification carried out using a classical speaker recognition system based on MFCC (Mel Frequency Cepstral Coefficients) parameterisation and GMM (Gaussian Mixture Models) classification

Using a low-bit rate speech enhancement variable post-filter as a speech recognition system pre-filter to improve robustness to GSM speech

Includes bibliographical references.Performance of speech recognition systems degrades when they are used to recognize speech that has been transmitted through GS1 (Global System for Mobile Communications) voice communication channels (GSM speech). This degradation is mainly due to GSM speech coding and GSM channel noise on speech signals transmitted through the network. This poor recognition of GSM channel speech limits the use of speech recognition applications over GSM networks. If speech recognition technology is to be used unlimitedly over GSM networks recognition accuracy of GSM channel speech has to be improved. Different channel normalization techniques have been developed in an attempt to improve recognition accuracy of voice channel modified speech in general (not specifically for GSM channel speech). These techniques can be classified into three broad categories, namely, model modification, signal pre-processing and feature processing techniques. In this work, as a contribution toward improving the robustness of speech recognition systems to GSM speech, the use of a low-bit speech enhancement post-filter as a speech recognition system pre-filter is proposed. This filter is to be used in recognition systems in combination with channel normalization techniques

The individual and the system : Assessing the stability of the output of a semi-automatic forensic voice comparison system

Semi-automatic systems based on traditional linguistic-phonetic features are increasingly being used for forensic voice comparison (FVC) casework. In this paper, we examine the stability of the output of a semi-automatic system, based on the long-term formant distributions (LTFDs) of F1, F2, and F3, as the channel quality of the input recordings decreases. Cross-validated, calibrated GMM-UBM log likelihood-ratios (LLRs) were computed for 97 Standard Southern British English speakers under four conditions. In each condition the same speech material was used, but the technical properties of the recordings changed (high quality studio recording, landline telephone recording, high bit-rate GSM mobile telephone recording and low bit-rate GSM mobile telephone recording). Equal error rate (EER) and the log LR cost function (Cllr) were compared across conditions. System validity was found to decrease with poorer technical quality, with the largest differences in EER (21.66%) and Cllr (0.46) found between the studio and the low bit-rate GSM conditions. However, importantly, performance for individual speakers was affected differently by channel quality. Speakers that produced stronger evidence overall were found to be more variable. Mean F3 was also found to be a predictor of LLR variability, however no effects were found based on speakers’ voice quality profiles

The Effect of Narrow-Band Transmission on Recognition of Paralinguistic Information From Human Vocalizations

Practically, no knowledge exists on the effects of speech coding and recognition for narrow-band transmission of speech signals within certain frequency ranges especially in relation to the recognition of paralinguistic cues in speech. We thus investigated the impact of narrow-band standard speech coders on the machine-based classification of affective vocalizations and clinical vocal recordings. In addition, we analyzed the effect of speech low-pass filtering by a set of different cut-off frequencies, either chosen as static values in the 0.5-5-kHz range or given dynamically by different upper limits from the first five speech formants (F1-F5). Speech coding and recognition were tested, first, according to short-term speaker states by using affective vocalizations as given by the Geneva Multimodal Emotion Portrayals. Second, in relation to long-term speaker traits, we tested vocal recording from clinical populations involving speech impairments as found in the Child Pathological Speech Database. We employ a large acoustic feature space derived from the Interspeech Computational Paralinguistics Challenge. Besides analysis of the sheer corruption outcome, we analyzed the potential of matched and multicondition training as opposed to miss-matched condition. In the results, first, multicondition and matched-condition training significantly increase performances as opposed to mismatched condition. Second, downgrades in classification accuracy occur, however, only at comparably severe levels of low-pass filtering. The downgrades especially appear for multi-categorical rather than for binary decisions. These can be dealt with reasonably by the alluded strategies

An investigation into glottal waveform based speech coding

Coding of voiced speech by extraction of the glottal waveform has shown promise in improving the efficiency of speech coding systems. This thesis describes an investigation into the performance of such a system. The effect of reverberation on the radiation impedance at the lips is shown to be negligible under normal conditions. Also, the accuracy of the Image Method for adding artificial reverberation to anechoic speech recordings is established. A new algorithm, Pre-emphasised Maximum Likelihood Epoch Detection (PMLED), for Glottal Closure Instant detection is proposed. The algorithm is tested on natural speech and is shown to be both accurate and robust. Two techniques for giottai waveform estimation, Closed Phase Inverse Filtering (CPIF) and Iterative Adaptive Inverse Filtering (IAIF), are compared. In tandem with an LF model fitting procedure, both techniques display a high degree of accuracy However, IAIF is found to be slightly more robust. Based on these results, a Glottal Excited Linear Predictive (GELP) coding system for voiced speech is proposed and tested. Using a differential LF parameter quantisation scheme, the system achieves speech quality similar to that of U S Federal Standard 1016 CELP at a lower mean bit rate while incurring no extra delay