The goal of this paper is to investigate the potential of using phase based features for automatically detecting voice disorders. The decomposition leads to novel speech features that are extracted from the filter component of the phase spectrum. 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. This is supported by digit recognition experiments which show a substantial recognition accuracy rate improvement over prior multimicrophone speech. Phaseaware speech enhancement based on deep neural.
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. 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. Phase based parameters are good candidates to detect synthetic speech due to the usual phase information neglect of many speech processing techniques. Usefulness of phase in speech processing citeseerx. Signal processing speech signals were first masked by the ssn masker at 0 or 5 db snr. 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. 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.
The aversion toward using the phase spectrum can be accounted for by two primary reasons. However, with the recent development of deep neural network dnn based speech processing, e. 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. Jun 10, 2019 this is because phase information, which is half of the original speech, is ignored when discriminating between replay and genuine speech. Automatic recognition systems source separation speech enhancement automatic recognition. Phase importance in speech processing applications isca speech. As a consequence, the phase based signal processing is believed to be more troublesome than signal processing methods relying on spectral amplitudeonly. 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. Phasebased information for voice pathology detection. Speech and language processing stanford university. Pdf new acoustic features for continuous speech recognition based on the shortterm fourier phase spectrum are introduced for mono telephone. As a consequence, the phasebased signal processing is believed to be more troublesome than. 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.
Single channel phaseaware signal processing in speech. Speech processing is the study of speech signals and processing methods. It also discusses the research in phase based speech processing. Index termsmicrophone arrays, speech processing, speech recognition, timefrequency analysis. 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. Takes a look at the importance of phase in the design of speech processing systems. The objective of this paper is to demonstrate, both analytically and experimentally, that group delay based features are robust to additive noise.
How natural speech is represented in the auditory cortex constitutes a major challenge for cognitive neuroscience. Nevertheless, since the magnitudebased paradigms are prevailed in speech processing, even in the case of phasebased features. Isreali conference on vision and ai, ramat gan, isreal, december, pp. Speech communication technology is a key factor in humanmachine interaction, digital hearing aids, mobile telephony, and automatic speech speaker recognition. 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. An expanding body of work is showing that it can be usefully employed in a multitude of speech processing applications. The decomposition leads to novel speech features that are extracted from the. In most current approaches of speech processing, information is extracted from the magnitude spectrum. 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. 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.
Robust phasebased speech signal processing from source. Derivative of instantaneous frequency for voice activity. 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. Speech processing 2 speech processing speech is the most natural form of humanhuman communications. Recent researches, however, showed that phase information can be smartly employed in speech processing and visual processing. One of the most commonly used phase feature is the modified group delay mgd based feature. 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. On the importance of preemphasis and window shape in phase. For a good recent overview of phaseaware signal processing in singlechannel speech enhancement, we refer to gerkmann et al.
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. Phaseaware speech enhancement based on deep neural networks. Advances in phaseaware signal processing in speech communication. Block scheme of the proposed speech emotion recognition system, using phasebased feature extraction, outer product, the power and l2 normalisation, and svms. Request pdf phasebased information for voice pathology detection in most current approaches of speech processing, information is extracted from the magnitude spectrum. In this paper, we propose a phaseaware speech enhancement algorithm based on dnn. For example, spatial phase in an image is indicative of local features such as edges when considering. 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. In the majority of speech processing applications such as speakerspeech recognition systems and speech enhancement, cepstral features are always computed from shorttime amplitude spectra. The conventional vad algorithms 2426 mostly use the amplitude information to recognize the presence or absence of speech. A challenge of audio watermarking systems in which an acoustic path is involved is the robustness against microphone pickup in case of surrounding noise.
If the address matches an existing account you will receive an email with instructions to reset your password. In many speech processing applications, the spectral amplitude is the dominant information while the use of phase spectrum is not so widely spread. Us9922658b2 method and apparatus for increasing the. Synthetic speech detection using phase information. Speech analysis using instantaneous frequency deviation. Phasebased adaptive estimation of magnitudesquared coherence between turbofan internal sensors and farfield microphone signals jeffrey hilton miles t nasa john h. 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. This paper proposes a new technique of phase unwrapping which is based on two. Phasebased features have also been successfully used for synthesized and converted speech detection 23, 24. In various applications such as, speech recognition and. The magnitude spectrum is widely used in almost every corner of speech processing.
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. Speech and hearing research group spandh, university of shef. It is shown that group delay functions are appropriate for characterizing. In other words, phase spectrum seems to have something more than what is captured by these features. In ieee international conference on acoustics speech and signal processing icassp pp. However, the phase spectrum is not an obviously appealing start point for processing the speech signal. Exploitation of phasebased features for whispered speech. As illustrated in figure 1, the group delaybased estimations of the. Comparing the contributions of amplitude and phase to speech. Robustness of phase based features for speaker recognition. Phase information can be analyzed in many ways instantaneous phase, shortterm group delay banno et al. Speech processing an overview sciencedirect topics.
Goal and scope i demonstrating the importance of phase in di. This is because phase information, which is half of the original speech, is ignored when discriminating between replay and genuine speech. The major problem in phase signal processing is the phase wrapping in the spectral fourier analysis. The chapter is targeted at making spectral phase accessible for researchers working on speech signal processing. Incorporating information from the shorttime phase spectrum into a feature set for automatic speech recognition asr may possibly serve to improve. Impact of phase estimation on singlechannel speech separation. Further, this knowledge will be useful in understanding the phase.
In this paper, we propose a method for parametric modeling of the phase spectrum, and discuss its applications in speech signal processing. As a complex quantity, it can be expressed in the polar form using the magnitude and phase spectra. Study of phasebased parametrisation of speech has resulted in several representations including the modi. One of the most commonly used phase feature is the modified group delay mgdbased feature. 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. More and more speech technology and signal processing applications make use of the phase information. Advances in nonlinear speech processing pp 160167 cite as on the importance of preemphasis and window shape in phase based speech recognition. Combining amplitude and phasebased featur es for speaker. With the proliferation of these applications, there is a growing requirement for advanced methodologies that can push the limits of the conventional solutions. The linguistics main office will be operating online through april 24.
Please contact staff and advisors who are available monday through friday 8 am to 5 pm. 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. Pdf using phase spectrum information for improved speech. Sv systems and new results from a proposed synthetic speech detector ssd which uses phasebased features for classi. 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. Oct 21, 2016 in this chapter, the objective is to provide a compilation of practical concepts and useful analysis tools for phase. This book highlights some of the important ways in which the phase of speech signals can be utilized for sound localization, enhancement, and recognition. Phasebased dualmicrophone robust speech enhancement. 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. In this paper, we propose a phase aware speech enhancement algorithm based on dnn. Phasebased speech processing takes a look at the importance of phase in the design of speech processing systems. Pdf analysis of phase spectrum of speech signals using allpass. International conference on nonlinear speech processing nolisp 20.
The goal of this paper is to investigate the potential of using phasebased features for automatically detecting voice disorders. Nowadays, a variety of approaches to the frequency and phase estimation problem, distinguished primarily by estimation accuracy, computational complexity, and. 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. The fourier analysis plays a key role in speech signal processing. Introduction most speech processing applications are based on the shorttime magnitude spectrum, while relatively little attention is paid to the shorttime phase spectrum. 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. Phasebased adaptive estimation of magnitudesquared. Most of the used digital processing approaches of speech signals exploit a short time fourier transform ft. Speech is related to human physiological capability.
Deng et al exploitation of phasebased features for whispered speech emotion recognition figure 1. Dec 12, 2017 we have proposed three phase based features for the language recognition task. 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. 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. Phase importance in speech processing applications uef. 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. To this end, the general problem of learning a filterbank consisting of modulated kernelbased baseband filters is studied. Research article a motion detection algorithm using local. 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. 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. On the importance of phase in human speech recognition, ieee transactions on audio, speech and language processing, 14 5, sep.
Therefore, preemphasis appears not to be a much needed block in phasebased speech processing. Language identification using phase information springerlink. To this end, the general problem of learning a filterbank consisting of modulated kernel based baseband filters is studied. In this domain, signal is represented with complex. First, we train two different sv systems gmmubm and svm using gmm supervectors using human speech 283 speakers from the wsj corpus. This paper analyses this spectrum and the proposed representation by evaluating statistical properties at various points along the parametrisation pipeline. 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. 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. More recently, we have preliminarily demonstrated the usefulness of phasebased features for whispered speech emotion recognition in 46. Phase based features have also been successfully used for synthesized and converted speech detection 23, 24. Phase processing for singlechannel speech enhancement.
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. As a consequence, the phasebased signal processing is believed to be more troublesome than signal processing methods relying on spectral amplitudeonly. Advances in phaseaware signal processing in speech. On the importance of preemphasis and window shape in. Glenn research center at lewis field, cleveland, oh 445. Esca workshop on speech processing in adverse conditions, cannes, november, pp. 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. Replay attack detection with auditory filterbased relative. 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. Speech is also related to sound and acoustics, a branch of physical. 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. Phasebased methods for fourier shape matching vision. Fast and accurate phase unwrapping semantic scholar. Nevertheless, since the magnitudebased paradigms are prevailed in speech processing, even in the case of phasebased features, preemphasis is used, without any modification. In this chapter, the objective is to provide a compilation of practical concepts and useful analysis tools for phase. Martin draft chapters in progress, october 16, 2019. 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. An experimental study on the phase importance in digital.
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