Spectro temporal gabor features for speaker recognition book

These physiologically and psychoacoustically motivated features employ spectrotemporal information inherent to the speech signal. Twodimensional gabor filter functions are applied to a spectro temporal representation formed by columns of primary feature vectors. Lpcc and ssc features masood qarachorloo and gholamreza farahani. We conclude with the some future areas in which the work can be done in order to extract efficient speech features to increase the accuracy of speaker recognition system. Automatic speech emotion recognition using machine. Mfccs and gabor features for improving continuous arabic. Spectro temporal gabor filterbank features for acoustic event detection. By deriving secondary features from the output of a perception model the tuning of neurons towards di. The hypothesis was that the integration of speech fragments distributed over frequency, time, and ear of presentation is reduced in older listenerseven for those with good audiometric hearing.

The speech databases that are used for the asr experiments aiming at the analyses of either intrinsic or extrinsic factors in speech are presented in section 2. Theoretical definition, categorization of affective state and the modalities of emotion expression are presented. Speaker identification apis allow you to identify who is speaking based on their voice, supporting scenarios such as conversation transcription. New features to improve speaker recognition efficiency. This paper presents results from recent studies utilizing spectro temporal gabor features for different tasks in automatic speech recognition 20, 21, 23 and is structured as follows. From the viewpoint of their physical interpretation, we can divide them into 1 shortterm spectral features, 2 voice source features, 3 spectrotemporal features, 4 prosodic features and 5 highlevel features. Aes elibrary extraction of spectrotemporal speech cues. Sign in sign up spectrotemporal gabor filterbank features for acoustic event detection. Multistream spectrotemporal features for robust speech. In this paper we investigate the applicability of spectro. Other biologically inspired spectrotemporal speech features, e.

Optimization of gabor features for textindependent speaker. Speaker recognition benchmark using the chime5 corpus. Zhao 1, nelson morgan 1,2 1 international computer science institute, berkeley, ca, usa 2 eecs department, university of california at berkeley, berkeley, ca, usa. When speaker recognition is used for surveillance applications or in general when the subject is not aware of it then the common privacy concerns of identifying unaware subjects apply. Stateoftheart mel frequency cepstral coefficients mfcc features are known to be affected by acoustic noise whereas physiologically motivated features such as spectrotemporal gabor filterbank gbfb features intend to perform better in signal degradation conditions. As a starting point, the properties of lstf features 1 are evaluated. Detection of acoustic events by using mfcc and spectro. Gabor features have been used mainly for automatic speech.

For classification we use the decision tree algorithm that gives better classification and detection result. Spectrotemporal gabor features for speaker recognition. Optimization of gabor features for textindependent. Speechnonspeech discrimination based on spectrotemporal. Pdf spectrotemporal gabor features for speaker recognition. Part of the lecture notes in computer science book series lncs, volume 8509. Automatic recognition of speech emotion using longterm spectrotemporal features siqing wu, tiago h. The api can be used to determine the identity of an unknown speaker. The resulting features showed a close resemblance to the strfs of cortical neurons in the auditory system.

Speaker recognition introduction speaker, or voice, recognition is a biometric modality that uses an individuals voice for recognition purposes. Feature extraction techniques in speaker recognition. Shortterm spectral features, as the name suggests, are computed from short frames of about 2030 ms in duration. Spectrotemporal directional derivative features for. Spectrotemporal refers most commonly to audition, where the neurons response depends on frequency versus time, while spatiotemporal refers to vision, where the neurons response depends on spatial location versus time. Noise robust automatic speech recognition based on spectrotemporal techniques summary of the phd dissertation. The gabor wavelet is the most common of these directional. The following page provides an overview of publications, including books, journal papers, conference proceedings as well as dissertations and research reports, published by researches of the fraunhofer institute for digital media technology idmt. Speaker recognition in a multispeaker environment alvin f martin, mark a. Pdf in this work, we have investigated the performance of 2d gabor features known as spectrotemporal features for speaker recognition. In this paper, we proposed a feature extraction method based on 59 twodimensional gabor filterbank. Speaker recognition introduction measurement of speaker characteristics construction of speaker models decision and performance applications this lecture is based on rosenberg et al. The recognition performance of our feature extraction method is evaluated in isolated words extracted from timit corpus. Localized spectrotemporal gabor features for automatic speech recognition the strf of cortical neurons and early auditory features.

Spectrotemporal analysis of speech using 2d gabor filters mit. We also discuss the problems associated with wellknown methods of feature extraction. Harmonicaligned frame mask based on nonstationary gabor transform with application to contentdependent speaker comparison. Bottleneck features for speaker recognition sibel yaman1, jason pelecanos1, ruhi sarikaya2 1 ibm t. Arraybased spectro temporal masking for automatic speech recognition submitted in partial ful llment of the requirements for the degree of doctor of philosophy in electrical and computer engineering amir r. Gabor features have been used mainly for automatic speech recognition asr, where they have yielded improvements. Instead, the 2d gabor outputs were lumped together into a onedimensional feature vector for use in recognition experiments. Improved deep speaker feature learning for textdependent speaker recognition lantian li, yiye lin, zhiyong zhang, dong wang center for speech and language technologies, division of technical innovation and development tsinghua national laboratory for information science and technology. Speaker verification apis serve as an intelligent tool to help verify speakers using both their voice and speech passphrases. Spectrotemporal gabor features as a front end for automatic. Multilayerperceptron used, pretrained with restricted boltzman machine. Simulation results on a database show that spectrotemporal features achieve higher recognition rates than purely temporal features for clean speech as well as for disturbed speech. An overview of textindependent speaker recognition. This paper presents results from recent studies utilizing spectrotemporal gabor features for different tasks in automatic speech recognition 20, 21, 23 and is structured as follows.

For textindependent speaker identification a prominent combination is to use gaussian mixture models gmm for classification while relying on melfrequency cepstral coefficients mfcc as features. We explored different gabor feature implementations, along with different speaker recognition approaches, on rossi 1 and nist sre08. Robustness of spectrotemporal features against intrinsic and. Neurophysiological studies suggest that the response of neurons in the primary auditory cortex of mammals are tuned to specific spectrotemporal patterns theunissen2001. On the suitability of the riesz spectrotemporal envelope for wavenet based speech synthesis jitendra kumar dhiman, nagaraj adiga, chandra sekhar seelamantula. The experiments with the htk recognizer were performed with different snrs matched training and testing. To achieve this study, an ser system, based on different classifiers and different methods for features extraction, is developed. In this work we built a lstm based speaker recognition system on a dataset collected from cousera lectures. Localized spectrotemporal features for automatic speech. We explored different gabor feature implementations, along with different speaker recognition approaches, on rossi 1 and nist sre08 databases. Spectro temporal refers most commonly to audition, where the neurons response depends on frequency versus time, while spatio temporal refers to vision, where the neurons response depends on spatial location versus time. Robust speech recognition based on spectrotemporal features.

As suggested by, the strf can be effectively modelled by twodimensional 2d gabor functions. Nov 26, 2018 physics being a function of both time and frequency or wavelength. Spectrotemporal gabor filterbank features for acoustic event. Improved deep speaker feature learning for textdependent. Przybocki national institute of standards and technology gaithersburg, md 20899 usa alvin. Detection of acoustic events by using mfcc and spectrotemporal gabor filterbank features. Noise robust automatic speech recognition based on. In this paper we investigate the applicability of spectrotemporal features obtained from gaborfilters and present an algorithm for optimizing the possible parameters.

Robust automatic speech recognition and modeling of auditory. Speaker verification also called speaker authentication contrasts with identification, and speaker recognition differs from speaker diarisation recognizing when the same speaker is speaking. Institute of electrical engineering and information technology, iranian research organization for science and. We propose to use the gabor filterbank in addition to mfccs coefficients to analyze the feature. Methods for capturing spectrotemporal modulations in. Novel gammatone filterbank based spectrotemporal features. Exploring spectrotemporal features in endtoend convolutional. Other prosodic features for speaker recognition have included.

Asr phoneme recognition rates for different speaking rates, efforts and style for mfcc and spectro temporal gabor features, obtained with the oldenburg logatome corpus. These features are then combined to obtain joint spectro temporal features which are used for posterior based speech recognition system. Pdf optimization of gabor features for textindependent. This chapter presents a comparative study of speech emotion recognition ser systems. Spectrotemporal gabor features for speaker recognition ieee xplore. Falk, and waiyip chan department of electrical and computer engineering queens university,kingston, ontario, canada siqing. Informative spectrotemporal bottleneck features for noiserobust speech recognition. Arraybased spectrotemporal masking for automatic speech. Extraction of prosodic features for speaker recognition. Spectrotemporal modulation spectrogram neurophysiological studies suggest that the response of neurons in the primary auditory cortex of mammals are tuned to specific spectrotemporal patterns theunissen2001. Asr phoneme recognition rates for different speaking rates, efforts and style for mfcc and spectrotemporal gabor features, obtained with the oldenburg logatome corpus.

Multistream spectrotemporal features for robust speech recognition sherry y. Gabor filters with high temporal modulation encode the most relevant information. Recognizing the speaker can simplify the task of translating speech in systems that have been trained on specific voices or it can be used to. On the relevance of auditorybased gabor features for deep.

Robustness of spectrotemporal features against intrinsic. Average values were calculated by averaging over all snr conditions. First, triangular filters can be replaced with gabor filters, a compactly supported. Modelling, feature extraction and effects of clinical environment a thesis submitted in fulfillment of the requirements for the degree of doctor of philosophy sheeraz memon b. This metric is used to explain the improved results on phoneme level. Mfcc is a technique commonly used for features extraction of speech and acoustic event. Input audio of the unknown speaker is paired against a group of selected speakers, and if a match is found, the speakers identity is returned. Feature extraction choosing which features to extract from speech is the most significant part of speaker recognition. Multistream spectro temporal features for robust speech recognition sherry y. Features extraction gabor filterbank robust speech recognition. Speaker recognition is unobtrusive, speaking is a natural process so no unusual actions are required. Melfrequency cepstrum coefficients mfcc and modulation. Physiologically motivated feature extraction methods based on 2dgabor filters have already been used successfully in robust automatic speech recognition.

Spectrotemporal analysis of speech using 2d gabor filters. In this paper, the localized spectrotemporal features lstf are analyzed further with. An ai service that enables you to identify individual speakers or use speech as a means of verification. Experiments with the proposed features for a phoneme recognition task in timit database is reported in sec. Pdf normalization of spectrotemporal gabor filter bank features. Similar techniques are widely used in the visual domain. An emerging technology, speaker recognition is becoming wellknown for providing voice authentication over the telephone for helpdesks. In this work, we have investigated the performance of 2d gabor features known as spectro temporal features for speaker recognition. Communication systems and networks school of electrical and computer engineering. In this work, i have concentrated on mfccs and lpcs. There are two approaches to exploring the prosodic features first is pitch and energy sharing here a feature vector consisting of per frame log pitch, log energy and their first derivatives was used for speaker verification.

The joint spectro temporal features adaptively capture. Spectrotemporal gabor filterbank features for acoustic. Dnnbased speech recognition greatly benefits from spectrotemporal gabor features. Pdf spectrotemporal gabor filterbank features for acoustic. As illustrated in figure 1, the input signal goes through a process consisting of dc removal, preemphasis, hamming windowing of 25ms in length with 10ms offset, fft. To take temporal information into account the time difference of features of adjacent speech frames are appended to the initial features. Introduction measurement of speaker characteristics. The spectro temporal receptive field or spatio temporal receptive field strf of a neuron represents which types of stimuli excite or inhibit that neuron. The spectrotemporal receptive field or spatiotemporal receptive field strf of a neuron represents which types of stimuli excite or inhibit that neuron. Stateoftheart mel frequency cepstral coefficients mfcc features are known to be affected by acoustic noise whereas physiologically motivated features such as spectro temporal gabor filterbank gbfb features intend to perform better in signal degradation conditions. This response characteristic of neurons can be described by the socalled strf. Speaker recognition or broadly speech recognition has been an active area of research for the past two decades. In this work, we have investigated the performance of 2d gabor features known as spectrotemporal features for speaker recognition. In kleinschmidt, 2002a the usage of 2dimensional gabor.

Noise robust automatic speech recognition based on spectro. The purpose of this study was to determine the effects of age on the spectrotemporal integration of speech. The use of these set of filters aims to extracting specific modulation frequencies and limiting the redundancy on feature level. The biologically inspired gabor feature sets proposed by him are shown. Use advanced ai algorithms for speaker verification and speaker identification. A measure of phoneme similarity is proposed to quantify class separability.

This concept of spectrotemporal modulation decomposition has inspired many approaches in various engineering topics, such as using spectrotemporal modulation features for speaker recognition 12, robust speech recognition 18, voice activity detection 10, and sound. A novel type of feature extraction is introduced to be used as a front end for automatic speech recognition asr. Pdf algorithms for the automatic detection and recognition of acoustic events are increasingly gaining relevance for the reliable and robust. Robust cnnbased speech recognition with gabor filter kernels.