Speech processing is emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. The objective of automatic speaker recognition is to extract, characterize and recognize the information about speaker identity. Feature extraction is the first step ...

Multimodal clustering/diarization tries to answer the question ”who spoke when” by using audio and visual information. Diarization consists of two steps, at first segmentation of the audio information and detection of the speech segments and then clustering of the speech segments to group the speakers. This task has been mainly studied on audiovisual data from meetings, news broadcasts or talk ...

This paper describes an automatic batch-style dictation paradigm in which the entire dictated speech is fully utilized for speaker adaptation and is recognized using the speaker adaptation results. The key point is that the same speech data is used both for recognition as the target and for speaker adaptation. Two steps, speech recognition and speaker adaptation which uses recognition results a...

Visual information is always combined as a complementary source to enhance the understanding of what the speaker is talking about, especially in a noisy environment. This paper researches on different lip features for visual speech and speaker recognition, and their robustness to different uttering habits is conducted in-depth analysis. Five feature candidates extracted from lip shape are teste...

This dissertation addresses the independence of observations assumption which is typically made by today’s automatic speech recognition systems. This assumption ignores within-speaker correlations which are known to exist. The assumption clearly damages the recognition ability of standard speaker independent systems, as can seen by the severe drop in performance exhibited by systems between the...

Speaker recognition is more active biometric task, which bases from the more general speech processing area. Likewise, most of the other speech-related recognition activities (language recognition, speech recognition), speaker recognition is a multidisciplinary problem. Both understanding of pattern recognition techniques and domain knowledge (Acoustics/Phonetics) are necessary. The motivation ...

In this paper we look at the parameterized feature-set that has been used in connected alpha-digit speech recognition and evaluate it on a speaker identification SID system. Compared to the popular mel-scaled featureset (MFCC) the parameterized feature-set gives over 21% improvement in identification rate on the NTIMIT database in some cases. On average it has a 14.0% improvement. This demonstr...

In this paper we discuss a speaker identification approach, called ASR-dependent speaker identification, that incorporates phonetic knowledge into the models for each speaker. This approach differs from traditional methods for performing textindependent speaker identification, such as global Gaussian mixture modeling, that typically ignore the phonetic content of the speech signal. We introduce...

With proper training, automatic speech recognition works quite well when tested in conditions similar to the training conditions, but with a new speaker or a new environment the system performance often degrades. Speaker-based adaptation alters the speech recognition system to better match a specific speaker and thus improves the speech recognition results. In order to use speaker adaptation, t...

Speech processing has emerged as one of the important application area of digital signal processing. Various fields for research in speech processing are speech recognition, speaker recognition, speech synthesis, speech coding etc. Speaker recognition is one of the most useful and popular biometric recognition techniques in the world especially related to areas in which security is a major conc...