High level features such as phone and word n-grams have been shown to be effective for speaker recognition, particularly when used along side traditional acoustic speaker recognition techniques. The applicability of these high-level recognition systems is impeded by the large training data requirements needed to build robust and stable speaker models. This paper describes an extension to an exi...

This chapter overviews techniques for evaluating speech and speaker recognition systems. The chapter first describes principles of recognition methods, and specifies types of systems as well as their applications. The evaluation methods can be classified into subjective and objective methods, among which the chapter focuses on the latter methods. In order to compare/normalize performances of di...

Information of phone relationships is regarded as acting an important role in speech recognition. It has been successfully exploited in many speaker adaptation approaches. In this paper, we propose a new approach, named Phone Pair Model (PPM) re-scoring, to utilize phone relationships for speaker-adaptive speech recognition. PPM re-scoring approach does not really adapt model parameters to a ne...

Presently, speaker adaptive systems are the state-of-theart in automatic speech recognition. A general baseline model is adapted to the current speaker during recognition in order to improve the quality of the results obtained. However, the adaptation procedure needs to be able to distinguish between data from different speakers. Therefore, in a general speaker adaptive recognizer speaker recog...

We describe the systems submitted to the NIST RT06s evaluation for the Speech Activity Detection (SAD) and Speaker Diarization (SPKR) tasks. For speech activity detection, a new analysis methodology is presented that generalizes the Detection Erorr Tradeoff analysis commonly used in speaker detection tasks. The speaker diarization systems are based on the TNO and ICSI system submitted for RT05s...

This article presents a low-latency speaker diarization system (“who is speaking now?”) based on a hybrid approach that combines a traditional offline speaker diarization system (“who spoke when?”) with an online speaker identification system. The system fulfills all requirements of the diarization task, i.e. it does not need any a-priori information about the input, including no specific speak...

Inter-speaker variation can be coped rather well in speech recognition by speaker adaptation techniques such as MLLR and MAP. However, when dealing with speech other than reading style, such as conversational speech, emotional speech and so on, current recognition systems cannot achieve a satisfactory performance even after speaker adaptation. In view of this situation, two-level adaptation met...

This paper deals with the task of speaker recognition in fourwire training and two-wire testing conditions. Instead of performing blind speaker diarization before the recognition stage, we directly perform the recognition on the nonsegmented (or imperfectly diarized) speech. We present an analysis of the problem with respect to three different speaker recognition systems and propose improved re...

Current text–to–speech systems are developed using studio-recorded speech in a neutral style or based on acted emotions. However, the proliferation of media sharing sites would allow developing a new generation of speech–based systems which could cope with spontaneous and styled speech. This paper proposes an architecture to deal with realistic recordings and carries out some experiments on uns...

The focus of this chapter is on facilities and network access-control applications of speaker recognition. Speech processing is a diverse field with many applications. Figure 8.1 shows a few of these areas and how speaker recognition relates to the rest of the field. This chapter will emphasize the speaker recognition applications shown in the boxes of Figure 8.1. Speaker recognition encompasse...