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

A Comparison of Front-Ends for Bitstream-Based ASR over IP

Automatic speech recognition (ASR) is called to play a relevant role in the provision of spoken interfaces for IP-based applications. However, as a consequence of the transit of the speech signal over these particular networks, ASR systems need to face two new challenges: the impoverishment of the speech quality due to the compression needed to fit the channel capacity and the inevitable occurrence of packet losses. In this framework, bitstream-based approaches that obtain the ASR feature vectors directly from the coded bitstream, avoiding the speech decoding process, have been proposed ([S.H. Choi, H.K. Kim, H.S. Lee, Speech recognition using quantized LSP parameters and their transformations in digital communications, Speech Commun. 30 (4) (2000) 223–233. A. Gallardo-Antolín, C. Pelàez-Moreno, F. Díaz-de-María, Recognizing GSM digital speech, IEEE Trans. Speech Audio Process., to appear. H.K. Kim, R.V. Cox, R.C. Rose, Performance improvement of a bitstream-based front-end for wireless speech recognition in adverse environments, IEEE Trans. Speech Audio Process. 10 (8) (2002) 591–604. C. Peláez-Moreno, A. Gallardo-Antolín, F. Díaz-de-María, Recognizing voice over IP networks: a robust front-end for speech recognition on the WWW, IEEE Trans. Multimedia 3(2) (2001) 209–218], among others) to improve the robustness of ASR systems. LSP (Line Spectral Pairs) are the preferred set of parameters for the description of the speech spectral envelope in most of the modern speech coders. Nevertheless, LSP have proved to be unsuitable for ASR, and they must be transformed into cepstrum-type parameters. In this paper we comparatively evaluate the robustness of the most significant LSP to cepstrum transformations in a simulated VoIP (voice over IP) environment which includes two of the most popular codecs used in that network (G.723.1 and G.729) and several network conditions. In particular, we compare ‘pseudocepstrum’ [H.K. Kim, S.H. Choi, H.S. Lee, On approximating Line Spectral Frequencies to LPC cepstral coefficients, IEEE Trans. Speech Audio Process. 8 (2) (2000) 195–199], an approximated but straightforward transformation of LSP into LP cepstral coefficients, with a more computationally demanding but exact one. Our results show that pseudocepstrum is preferable when network conditions are good or computational resources low, while the exact procedure is recommended when network conditions become more adverse.Publicad

Speaker Recognition in the VoIP Environment

Tato práce popisuje použití systémů pro rozpoznávání mluvčího v~prostředí VoIP, úspěšnost systému a přístupy k jejímu zlepšení. Popisuje architekturu těchto systémů, metriky pro vyhodnocení jejich úspěšnosti a klíčové komponenty VoIP z hlediska rozpoznávání mluvčího. Je zde popsáno vytvoření simulace VoIP prostředí, úspěšnost systému je vyhodnocena na datech pocházejících z různých druhů VoIP prostředí a výsledky jsou demostrovány. Adaptace a kalibrace systému je provedena a jejich přínosy zhodnoceny.This work describes using speaker recognition systems in the VoIP environment, system performance and approaches to improving it. System architecture, evaluation metrics and VoIP technology key components from the view of speaker recognition are described. VoIP environment simulation is described. Speaker recognition system's performance is evaluated on data sets from various kinds of VoIP environments and the results are demonstrated. System adaptation and calibration is performed and their benefits are discussed.

Non-intrusive identification of speech codecs in digital audio signals

Speech compression has become an integral component in all modern telecommunications networks. Numerous codecs have been developed and deployed for efficiently transmitting voice signals while maintaining high perceptual quality. Because of the diversity of speech codecs used by different carriers and networks, the ability to distinguish between different codecs lends itself to a wide variety of practical applications, including determining call provenance, enhancing network diagnostic metrics, and improving automated speaker recognition. However, few research efforts have attempted to provide a methodology for identifying amongst speech codecs in an audio signal. In this research, we demonstrate a novel approach for accurately determining the presence of several contemporary speech codecs in a non-intrusive manner. The methodology developed in this research demonstrates techniques for analyzing an audio signal such that the subtle noise components introduced by the codec processing are accentuated while most of the original speech content is eliminated. Using these techniques, an audio signal may be profiled to gather a set of values that effectively characterize the codec present in the signal. This procedure is first applied to a large data set of audio signals from known codecs to develop a set of trained profiles. Thereafter, signals from unknown codecs may be similarly profiled, and the profiles compared to each of the known training profiles in order to decide which codec is the best match with the unknown signal. Overall, the proposed strategy generates extremely favorable results, with codecs being identified correctly in nearly 95% of all test signals. In addition, the profiling process is shown to require a very short analysis length of less than 4 seconds of audio to achieve these results. Both the identification rate and the small analysis window represent dramatic improvements over previous efforts in speech codec identification

DeepVoCoder: A CNN model for compression and coding of narrow band speech

This paper proposes a convolutional neural network (CNN)-based encoder model to compress and code speech signal directly from raw input speech. Although the model can synthesize wideband speech by implicit bandwidth extension, narrowband is preferred for IP telephony and telecommunications purposes. The model takes time domain speech samples as inputs and encodes them using a cascade of convolutional filters in multiple layers, where pooling is applied after some layers to downsample the encoded speech by half. The final bottleneck layer of the CNN encoder provides an abstract and compact representation of the speech signal. In this paper, it is demonstrated that this compact representation is sufficient to reconstruct the original speech signal in high quality using the CNN decoder. This paper also discusses the theoretical background of why and how CNN may be used for end-to-end speech compression and coding. The complexity, delay, memory requirements, and bit rate versus quality are discussed in the experimental results.Web of Science7750897508