Text-dependent speaker verification under noisy conditions using parallel model combination
نویسندگان
چکیده
In real speaker verification applications, additive or convolutive noise creates a mismatch between training and recognition environments, degrading performance. Parallel Model Combination (PMC) is used successfully to improve the noise robustness of Hidden Markov Model (HMM) based speech recognisers [5]. This paper presents the results of applying PMC to compensate for additive noise in HMM-based text-dependent speaker verification. Speech and noise data were obtained from the YOHO [6] and NOISEX-92 databases [13] respectively. Speaker recognition Equal Error Rates (EER) are presented for noise-contaminated speech at different signal-to-noise ratios (SNRs) and different noise sources. For example, average EER for speech in operations room noise at 6dB SNR dropped from approximately 20% un-compensated to less than 5% using PMC. Finally, it is shown that speaker recognition performance is relatively insensitive to the exact value of the parameter that determines the relative amplitudes of the speech and noise components of the PMC model.
منابع مشابه
Speaker-Dependent Dictionary-Based Speech Enhancement for Text-Dependent Speaker Verification
The problem of text-dependent speaker verification under noisy conditions is becoming ever more relevant, due to increased usage for authentication in real-world applications. Classical methods for noise reduction such as spectral subtraction and Wiener filtering introduce distortion and do not perform well in this setting. In this work we compare the performance of different noise reduction me...
متن کاملCombination of clean and contaminated GMM/SVM for far-field text-independent speaker verification
This paper addresses the problem of speaker verification under reverberant conditions, using only the signal acquired by a single distant microphone. The proposed system combines four different subsystems. Two of them are Gaussian Mixture Model (GMM) based and the other two are Support Vector Machine (SVM) based. The subsystems that use the same type of classifier differ in terms of models: one...
متن کاملNoise-robust multi-stream fusion for text-independent speaker authentication
Multi-stream approaches have proven to be very successful in speech recognition tasks and to a certain extent in speaker authentication tasks. In this study we propose a noiserobust multi-stream text-independent speaker authentication system. This system has two steps: first train the stream experts under clean conditions and then train the combination mechanism to merge the scores of the strea...
متن کاملParallel Speaker and Content Modelling for Text-Dependent Speaker Verification
Text-dependent short duration speaker verification involves two challenges. The primary challenge of interest is the verification of the speaker’s identity, and often a secondary challenge of interest is the verification of the lexical content of the pass-phrase. In this paper, we propose the use of two systems to handle these two tasks in parallel with one subsystem modelling speaker identity ...
متن کاملSpeech detection for text-dependent speaker verification
The performance of text-dependent speaker verification systems degrades in noisy environment and when the true speaker utters words that are not part of the verification password. Energy-based voice activity detection (VAD) algorithms cannot distinguish between the true speaker’s speech and other background speech or between the speaker’s verification password and other words uttered by the spe...
متن کامل