Takes a look at the importance of phase in the design of speech processing systems. Replay attack detection with auditory filterbased relative. Now that the potential for the phase based speech processing has been established, there is a need for a fundamental model to help understand the way in which phase encodes speech information. Speech and language processing stanford university. This book also discusses the stateoftheart research in phase based speech processing, starting from the basics of signal processing and recording, to single microphone speech. International conference on nonlinear speech processing nolisp 20. Phase importance in speech processing applications uef. Phase importance in speech processing applications isca speech.
In this domain, signal is represented with complex. Fast and accurate phase unwrapping semantic scholar. Advances in nonlinear speech processing pp 160167 cite as on the importance of preemphasis and window shape in phase based speech recognition. The magnitude spectrum is widely used in almost every corner of speech processing. The strength of phasebased watermarking is increased by determining a masking threshold for a current frequency bin in a frequencyphase representation changing the phase based on that masking threshold and an allowed phase change. An experimental study on the phase importance in digital. Comparing the contributions of amplitude and phase to speech.
This is because phase information, which is half of the original speech, is ignored when discriminating between replay and genuine speech. Advances in phaseaware signal processing in speech communication. In the majority of speech processing applications such as speaker speech recognition systems and speech enhancement, cepstral features are always computed from shorttime amplitude spectra. The signals are usually processed in a digital representation, so speech processing can be regarded as a special case of digital signal processing, applied to speech signals. However, with the recent development of deep neural network dnn based speech processing, e.
A representation based on frequencies of the speech signal derived from its shorttime phase is developed and is found to be as good as a cepstral representation. Oct 21, 2016 in this chapter, the objective is to provide a compilation of practical concepts and useful analysis tools for phase. In fact, phase wrapping has been the main reason that phasebased signal processing has been considered less often in the literature on speech signal processing. Advances in phaseaware signal processing in speech. Nevertheless, since the magnitudebased paradigms are prevailed in speech processing, even in the case of phasebased features. Second, we create synthetic test speech for each of the 283 speakers by adapting. Speech processing 2 speech processing speech is the most natural form of humanhuman communications. One of the most commonly used phase feature is the modified group delay mgd based feature. Pdf on learning interpretable cnns with parametric. It also discusses the research in phase based speech processing. However recent perceptual studies have underlined the importance of the phase component. Their result showed that the intelligibility of phase based speech was significantly improved when using a high. Index termsmicrophone arrays, speech processing, speech recognition, timefrequency analysis.
First, we train two different sv systems gmmubm and svm using gmm supervectors using human speech 283 speakers from the wsj corpus. Vad detects the presence or absence of human speech and plays an important role in speech processing, especially in speech coding 22 and speech recognition 23. Feb 28, 2006 thus, this book highlights some of the important ways in which the phase of speech signals can be utilized for sound localization, enhancement, and recognition. To this end, the general problem of learning a filterbank consisting of modulated kernel based baseband filters is studied. It is shown that by masking the tf representation of the speech signals, the noise components are distorted beyond recognition while the speech source of interest maintains its perceptual quality. A parallel pointprocess filter for estimation of goaldirected movements from neural signals, in proceedings of ieee international conference on acoustics, speech and signal processing icassp, dallas, usa, 2010. For a good recent overview of phaseaware signal processing in singlechannel speech enhancement, we refer to gerkmann et al. In most current approaches of speech processing, information is extracted from the magnitude spectrum. Martin draft chapters in progress, october 16, 2019. Phase based parameters are good candidates to detect synthetic speech due to the usual phase information neglect of many speech processing techniques. Glenn research center at lewis field, cleveland, oh 445. Language identification using phase information springerlink.
Speech and hearing research group spandh, university of shef. Isreali conference on vision and ai, ramat gan, isreal, december, pp. Derivative of instantaneous frequency for voice activity. We investigate the problem of direct waveform modelling using parametric kernel based filters in a convolutional neural network cnn framework, building on sincnet, a cnn employing the cardinal sine sinc function to implement learnable bandpass filters. With the proliferation of these applications, there is a growing requirement for advanced methodologies that can push the limits of the conventional solutions relying on processing the signal magnitude spectrum. The fourier analysis plays a key role in speech signal processing.
In this paper, we propose a phase aware speech enhancement algorithm based on dnn. Introduction i n various applications such as, speech recognition and automatic teleconferencing, the recorded speech signals may be corrupted by noises which can include gaussian noise, speech noise unrelated conversations, and reverberation 19. The decomposition leads to novel speech features that are extracted from the filter component of the phase spectrum. Pdf using phase spectrum information for improved speech. Exploitation of phasebased features for whispered speech. Study of phasebased parametrisation of speech has resulted in several representations including the modi. Block scheme of the proposed speech emotion recognition system, using phasebased feature extraction, outer product, the power and l2 normalisation, and svms. To synthesize the amplitude and phase based vocoded stimuli, a preemphasis highpass filter 2000 hz cutoff with a 3 dboctave rolloff was used to process the speech signals. In this chapter, the objective is to provide a compilation of practical concepts and useful analysis tools for phase. Speech is also related to sound and acoustics, a branch of physical. This book also discusses the stateoftheart research in phasebased speech processing, starting from the basics of signal processing and recording, to single microphone speech recognition, the recognition of speech and the processing of speech by humans, as well as the importance of phase in human speech recognition and multimicrophone phase.
With the proliferation of these applications, there is a growing requirement for advanced methodologies that can push the limits of the conventional solutions. As a consequence, the phasebased signal processing is believed to be more troublesome than. If the address matches an existing account you will receive an email with instructions to reset your password. The major problem in phase signal processing is the phase wrapping in the spectral fourier analysis. The conventional vad algorithms 2426 mostly use the amplitude information to recognize the presence or absence of speech. It is shown that group delay functions are appropriate for characterizing.
The interspeech 2014 special session on phase importance in speech processing applications organized by the authors in this paper aims to promote the phasebased speech signal pro. The neglected and important point which should be noted is that due to the predominant role of the magnitude spectrum in speech processing, common stages of. This paper analyses this spectrum and the proposed representation by evaluating statistical properties at various points along the parametrisation pipeline. Synthetic speech detection using phase information. Phase information can be analyzed in many ways instantaneous phase, shortterm group delay banno et al. Us9922658b2 method and apparatus for increasing the. An overview on the challenging new topic of phase aware signal processing speech communication technology is a key factor in humanmachine interaction, digital hearing aids, mobile telephony, and automatic speech speaker recognition. Combining amplitude and phasebased featur es for speaker. Automatic language analysis and identification based on speech production knowledge. Phaseaware speech enhancement based on deep neural networks. Dec 12, 2017 we have proposed three phase based features for the language recognition task.
In the smart antenna system and speech processing system, a poor phase estimator may cause the system to fail to identify the direction of arrival of the signal 6, 7. Speech processing is the study of speech signals and processing methods. In this paper, we propose a method for parametric modeling of the phase spectrum, and discuss its applications in speech signal processing. In other words, phase spectrum seems to have something more than what is captured by these features. Please contact staff and advisors who are available monday through friday 8 am to 5 pm. The chapter is targeted at making spectral phase accessible for researchers working on speech signal processing.
As a complex quantity, it can be expressed in the polar form using the magnitude and phase spectra. Goal and scope i demonstrating the importance of phase in di. Further, this knowledge will be useful in understanding the phase. One of the most commonly used phase feature is the modified group delay mgdbased feature.
Phase processing for singlechannel speech enhancement. Speech analysis using instantaneous frequency deviation. Incorporating information from the shorttime phase spectrum into a feature set for automatic speech recognition asr may possibly serve to improve. Sv systems and new results from a proposed synthetic speech detector ssd which uses phasebased features for classi. The decomposition leads to novel speech features that are extracted from the. A challenge of audio watermarking systems in which an acoustic path is involved is the robustness against microphone pickup in case of surrounding noise.
Speech is related to human physiological capability. Phasebased methods for voice source analysis 3 in the early years of the sourcefilter theory of speech production, the effect of the voice source was mainly studied in the spectral domain, like in equation 2. For example, spatial phase in an image is indicative of local features such as edges when considering. We investigate the problem of direct waveform modelling using parametric kernelbased filters in a convolutional neural network cnn framework, building on sincnet, a cnn employing the cardinal sine sinc function to implement learnable bandpass filters.
Phasebased methods for fourier shape matching vision. In ieee international conference on acoustics speech and signal processing icassp pp. Although many singleunit and neuroimaging studies have yielded valuable insights about the processing of speech and matched complex sounds, the mechanisms underlying the analysis of speech dynamics in human auditory cortex remain largely unknown. This paper proposes a new technique of phase unwrapping which is based on two. In fact, phase wrapping has been the main reason that phase based signal processing has been considered less often in the literature on speech signal processing. As illustrated in figure 1, the group delaybased estimations of the. Introduction most speech processing applications are based on the shorttime magnitude spectrum, while relatively little attention is paid to the shorttime phase spectrum. On the importance of preemphasis and window shape in. Phasebased information for voice pathology detection thomas drugman, thomas dubuisson, thierry dutoit tcts lab university of mons belgium abstract in most current approaches of speech processing, information is extracted from the magnitude spectrum. As a consequence, the phase based signal processing is believed to be more troublesome than signal processing methods relying on spectral amplitudeonly.
An overview on the challenging new topic of phaseaware signal processing speech communication technology is a key factor in humanmachine interaction, digital hearing aids, mobile telephony, and automatic speechspeaker recognition. Esca workshop on speech processing in adverse conditions, cannes, november, pp. This is supported by digit recognition experiments which show a substantial recognition accuracy rate improvement over prior multimicrophone speech. Thus, this book highlights some of the important ways in which the phase of speech signals can be utilized for sound localization, enhancement, and recognition. Speech communication technology is a key factor in humanmachine interaction, digital hearing aids, mobile telephony, and automatic speech speaker recognition. In this paper, we propose a phaseaware speech enhancement algorithm based on dnn. Request pdf phasebased information for voice pathology detection in most current approaches of speech processing, information is extracted from the magnitude spectrum.
Us20100323652a1 us12796,566 us79656610a us2010323652a1 us 20100323652 a1 us20100323652 a1 us 20100323652a1 us 79656610 a us79656610 a us 79656610a us 2010323652 a1 us2010323652 a1 us 2010323652a1 authority us united states prior art keywords channel multichannel signal calculated amplitude level prior art date 20090609 legal status the legal status. The objective of this paper is to demonstrate, both analytically and experimentally, that group delay based features are robust to additive noise. Jun 10, 2019 this is because phase information, which is half of the original speech, is ignored when discriminating between replay and genuine speech. Pdf new acoustic features for continuous speech recognition based on the shortterm fourier phase spectrum are introduced for mono telephone. Signal processing speech signals were first masked by the ssn masker at 0 or 5 db snr. On the importance of preemphasis and window shape in phase. The goal of this paper is to investigate the potential of using phase based features for automatically detecting voice disorders. More and more speech technology and signal processing applications make use of the phase information. As a consequence, the phasebased signal processing is believed to be more troublesome than signal processing methods relying on spectral amplitudeonly.
I consider the latest progress in phase based speech processing i establish a new community of researchers working on phase overview on phase importance in speech applications 1. Phasebased information for voice pathology detection. Phaseaware speech enhancement based on deep neural. An expanding body of work is showing that it can be usefully employed in a multitude of speech processing applications. Deng et al exploitation of phasebased features for whispered speech emotion recognition figure 1. Phase based information for voice pathology detection thomas drugman, thomas dubuisson, thierry dutoit tcts lab university of mons belgium abstract in most current approaches of speech processing, information is extracted from the magnitude spectrum.
Nowadays, a variety of approaches to the frequency and phase estimation problem, distinguished primarily by estimation accuracy, computational complexity, and. This book highlights some of the important ways in which the phase of speech signals can be utilized for sound localization, enhancement, and recognition. In the majority of speech processing applications such as speakerspeech recognition systems and speech enhancement, cepstral features are always computed from shorttime amplitude spectra. Phasebased dualmicrophone robust speech enhancement. In various applications such as, speech recognition and. Most of the used digital processing approaches of speech signals exploit a short time fourier transform ft. The aversion toward using the phase spectrum can be accounted for by two primary reasons. A proper estimation and representation of the phase goes inextricably along with a correct phase unwrapping, which refers to the problem of finding the instance of the phase function chosen to ensure continuity. Phase based methods for voice source analysis 3 in the early years of the sourcefilter theory of speech production, the effect of the voice source was mainly studied in the spectral domain, like in equation 2.
Phase based features have also been successfully used for synthesized and converted speech detection 23, 24. Phasebased speech processing takes a look at the importance of phase in the design of speech processing systems. This book also discusses the stateoftheart research in phasebased speech processing, starting from the basics of signal processing and recording, to single microphone speech recognition, the recognition of speech and the processing of speech by humans, as well as the importance of phase in human speech recognition and multimicrophone phasebased speech processing. Phasebased adaptive estimation of magnitudesquared coherence between turbofan internal sensors and farfield microphone signals jeffrey hilton miles t nasa john h. Phasebased features have also been successfully used for synthesized and converted speech detection 23, 24. However, the phase spectrum is not an obviously appealing start point for processing the speech signal. To this end, the general problem of learning a filterbank consisting of modulated kernelbased baseband filters is studied. Usefulness of phase in speech processing citeseerx. Pdf analysis of phase spectrum of speech signals using allpass. Recent researches, however, showed that phase information can be smartly employed in speech processing and visual processing. More recently, we have preliminarily demonstrated the usefulness of phasebased features for whispered speech emotion recognition in 46. Speech processing an overview sciencedirect topics. Therefore, preemphasis appears not to be a much needed block in phasebased speech processing. Phase processing or equivalently group delay processing of speech signals are known to be difficult due to large spikes in the phase group delay functions that mask the formant structure.
On the importance of phase in human speech recognition, ieee transactions on audio, speech and language processing, 14 5, sep. Automatic recognition systems source separation speech enhancement automatic recognition. Phasebased adaptive estimation of magnitudesquared. Impact of phase estimation on singlechannel speech separation. Nevertheless, since the magnitudebased paradigms are prevailed in speech processing, even in the case of phasebased features, preemphasis is used, without any modification. Research article a motion detection algorithm using local. How natural speech is represented in the auditory cortex constitutes a major challenge for cognitive neuroscience. Robustness of phase based features for speaker recognition. In many speech processing applications, the spectral amplitude is the dominant information while the use of phase spectrum is not so widely spread. This book also discusses the stateoftheart research in phase based speech processing, starting from the basics of signal processing and recording, to single microphone speech recognition, the recognition of speech and the processing of speech by humans, as well as the importance of phase in human speech recognition and multimicrophone phase. Robust phasebased speech signal processing from source. The linguistics main office will be operating online through april 24. Single channel phaseaware signal processing in speech. The goal of this paper is to investigate the potential of using phasebased features for automatically detecting voice disorders.
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