Speaker Features And Recognition Techniques : A Review 1

This paper gives an overview of various methods and techniques used for feature extraction and modeling in speaker recognition. The research in speaker recognition have been evolved starting from short time features reflecting spectral properties of speech (low-level or physical traits) to the high level features (behavioural traits) such as prosody, phonetic information, conversational patterns etc. Low level acoustic information such as cepstral features has been dominated as these features gives very low error rates (especially in quiet conditions). But they are more prone to error in noisy conditions. In this paper various features along with modeling techniques used in speaker recognition are discussed. Introduction Speech is the product of a complex behaviour conveying different speaker-specific traits that are potential sources of complementary information. Human speech production can be modeled by so-called source-filter model featured. As the name suggests, the model considers the voice production mechanism as a combination of two components: the voice source and the acoustic filter. The “source” refers to the airstream generated by the larynx and the “filter” refers to the vocal tract. Both of the components are inherently time-varying and assumed to be independent of each other [1], [2]. In this model we separate the source G(w) from the articulation and radiation H(w) (Furui S. 2001). The speech signal is then represented by the cascade connection of G(w) and H(w) giving: S (w) =G (w) H (w). For voiced speech, the source is modeled as an impulse train with period T. For unvoiced speech, the source is modeled as Gaussian white noise. In each case, the source is amplified by the gain factor G in proportion to the volume of speech. The signal is then passed through a time-varying digital filter to represent the articulation and radiation applied by the vocal track. Historically, all speaker recognition systems have been mainly based on acoustic cues that is nothing but physical traits extracted from spectral characteristics of speech signals. So far the features derived from the speech spectrum have proven to be the most effective in automatic systems, because the spectrum reflects the geometry of system that generate the signal. Therefore the variability in the dimensions of the vocal track is reflected in the variability of the spectra between the speakers. However, studies [3] have proved that there is a large amount of information suitable for speaker recognition being the top part related to learned traits and the bottom part to physical traits. Speaker Recognition One objective in automatic speaker recognition is to decide which voice model from a known set of voice models best characterizes a speaker; this task is referred to as speaker identification. In the different task of speaker verification, the goal is to decide whether a speaker corresponds to a particular known voice or to some other unknown voice. There are two modes of operation for speaker identification: in the closed-set mode, the system assumes that the unknown voice must come from the set of known voices; in open-set mode, the speakers that do not belong to the set of known voices are referred to as impostors. An important application of speaker identification technology is forensics, identifying the suspects among a set of known criminals. Automatic speaker recognition systems can be further classified according to the speech modality: text-dependent or text-independent. In text-dependent recognition, the user must speak a phrase known to the system, which can be fixed or prompted. The knowledge of a spoken phrase can provide better recognition results. In text-independent recognition, the system does not know the phrase spoken by the user. Despite the unconstrained phrase selection, this makes the system to be more complex. However, text-independent speaker recognition systems have more applications than text-dependent ones in real life. Dr. Mahesh S. Chavan , Mrs. Sharada V. Chougule, /International Journal Of Computational Engineering Research / ISSN: 2250–3005 IJCER | May-June 2012 | Vol. 2 | Issue No.3 |720-728 Page 721 There are generally two phases [1] in building or using a speaker recognition system. The first phase is called enrolment or training phase, in which a user enrols by providing voice samples to the system. The system extracts speaker-specific information from the voice samples to build a voice model of the enrolled speaker. The second phase is called the classification or recognition phase, in which a test voice sample is used by the system to measure the similarity of the user’s voice to the previously enrolled speaker models to make a decision. In a speaker identification task, the system measures the similarity of the test sample to all stored voice models. In speaker verification task, the similarity is measured only to the model of the claimed identity. The decision also differs across systems. For example, a closed-set identification task outputs the identity of the recognized user; besides the identity, an open-set identification task can also choose to reject the user in case the test sample do not belong to any of the stored voice models; a verification task chooses to accept or reject the identity claim. The effectiveness of speaker recognition system is in measures differently for different tasks. Since the output of a closed-set speaker identification system is a speaker identity from a set of known speakers, the identification accuracy is used to measure the performance. For open-set systems there are two types of errors: false acceptance of an impostor and false rejection of a known speaker. The performance measure can also incorporate the cost associated with each error. Like most pattern recognition problems, a speaker recognition system can be partitioned into four modules: feature analysis and extraction, speaker modelling, pattern matching and decision logic. Feature Analysis and Extraction From human speech production mechanism, it is possible to identify individual using the speech data. Speech contains speaker specific information due to vocal track and excitation source. Larynx is the major excitation source, whereas vocal track is the major resonant structure. Speaker information is due to particular shape, size and dynamics of vocal track and also the excitation source. These features related to physiological nature of human speech production are called physical traits, which are used in state-of-art systems. However human speaker recognition relies on other sources of information like speaking style, pronunciation etc. Such features are referred to as behavioral traits. Further, the behavioral traits like how the vocal tract and excitation source are controlled during speech production are also unique for each speaker. The information about the behavioral trait is also embedded into the speech signal and can be used for speaker recognition. Thus the information present in speech signal carries the identity of speaker at different levels. To properly represent speech data, it is necessary to analyse it using suitable analysis techniques. The analysis techniques aims at selecting proper frame size and shift for analysis and also at extracting the relevant features in the feature extraction stage [4]. The information about the audio category is contained in the excitation source (sub-segmental), system/physiological (segmental) and behavioral (suprasegmental) characteristics of the speech data. Sub-segmental analysis is one in speech signal is analysed using the frame size and shift of very small duration (3-5ms). This technique is used mainly to analyse and extract the characteristics of the excitation source. Since the excitation source information is relatively fast varying compared to the vocal tract information, small frame size and shift are required to best capture the speaker-specific information, which is the reason for the choice of 3-5 ms for frame size and shift [5]. In segmental analysis (used for extraction of short-term spectral features), features are computed from short frames of about 20-30 milliseconds in duration. They are usually descriptors of the short-term spectral envelope which is an acoustic correlate of timbre, i.e. the “colour” of sound, as well as the resonance properties of the supra-laryngeal vocal tract. High-level features are generally related to a speaker’s learned habits and style, such as particular word usage or idiolect. For humans, the information about the audio category is perceived by listening to a longer segment of audio signal. This other level of information contained in the audio signal is the suprasegmental information that is the variation of the signal over long duration. In this case, speech is analysed using the frame size and shift in the range of 50-200 ms. Studies made in [5],[6],[7] shows the significance of suprasegmental features in speaker recognition systems. These features are useful as their structure is not affected by the frequency characteristics of the transmission systems. Each of the four basic acoustic features of speech signal, i.e. pitch, intensity, duration and speech quality, is a carrier of a variety of types of linguistic, paralinguistic and non-linguistic information [8][9]. Feature Extraction Techniques Feature extraction is said to be the heart of speaker recognition system. Feature extraction is one where the input speech is processed to obtain those features of the input speech which are useful in speaker identification. The function of feature extraction is to convert speech waveform to some type of parametric representation called feature vectors for further analysis and processing by the classifiers. This stage extract the speaker-specific information in the form of feature vectors at reduced data rate. The feature vectors represent the speaker-specific information due to one or more of the followi