Linear discriminant - a new criterion for speaker normalization

This paper proposes a novel feature-space VTLN (vocal tract length normalization) method that models frequency warping as a linear interpolation of contiguous Mel filter-bank energies. The presented technique aims to reduce the distortion in the Mel filter-bank energy estimation due to the harmonic composition of voiced speech intervals and DFT (discrete Fourier transform) sampling when the central frequency of band-pass filters is shifted. This paper also proposes an analytical maximum likelihood (ML) method to estimate the optimal warping factor in the cepstral space. The presented interpolated filter-bank energy-based VTLN leads to relative reductions in WER (word error rate) as high as 11.2% and 7.6% when compared with the baseline system and standard VTLN, respectively, in a medium-vocabulary continuous speech recognition task. Also, the proposed VTLN scheme can provide significant reductions in WER when compared with state-of-the-art VTLN methods based on linear transforms in the cepstral feature-space. The warping factor estimated with the proposed VTLN approach shows more dependence on the speaker and more independence of the acoustic-phonetic content than the warping factor resulting from standard and state-of-the-art VTLN methods. Finally, the analytical ML-based optimization scheme presented here achieves almost the same reductions in WER as the ML grid search version of the technique with a computational load 20 times lower.

This paper shows the impact of speaker normalization techniques, such as vocal tract length normalization (VTLN) and speaker-adaptive training (SAT), prior to discriminant feature space transforms, such as LDA (linear discriminant analysis). We demonstrate that removing the inter-speaker variability by using speaker compensation methods results in improved discrimination as measured by the LDA eigenvalues and also in improved classification accuracy (as measured by the word error rate). Experimental results on the SPINE (speech in noisy environments) database indicate an improvement of up to 5% relative over the standard case where speaker adaptation (during testing and training) is applied after the LDA transform which is trained in a speaker independent manner. We conjecture that performing linear discriminant analysis in a canonical feature space (or speaker normalized space) is more effective than LDA in a speaker independent space because the eigenvectors carve a subspace of maximum intra-speaker phonetic separability whereas in the latter case this subspace is also defined by the inter-speaker variability. Indeed, we show that the more normalization is performed (first VTLN, then SAT), the higher the LDA eigenvalues become.

This paper describes the work done in improving the performance of Tamil speech recognition system by using Time Scale Modification (TSM) and Vocal Tract Length Normalization (VTLN) techniques. The speech recognition system for Tamil language was developed using a new approach of text independent speech segmentation, with a phoneme based language model for recognition. There is degradation in the performance of speech recognition due to variations in the speaking rate and vocal tract shape among different speakers. In order to improve the performance of speech recognition system, both TSM and VTLN normalization techniques were used in this work. The TSM was implemented using the Phase vocoder approach and the VTLN was implemented using speaker specific bark/mel scale in bark/mel domain. The performance of Tamil speech recognition system was improved by performing both TSM and VTLN normalization techniques.

Children’s Speech Recognition (CSR) is a challenging task due to the high variability in children’s speech patterns and limited amount of available annotated children’s speech data. We aim to improve CSR in the often-occurring scenario that no children’s speech data is available for training the Automatic Speech Recognition (ASR) systems. Traditionally, Vocal Tract Length Normalization (VTLN) has been widely used in hybrid ASR systems to address acoustic mismatch and variability in children’s speech when training models on adults’ speech. Meanwhile, End-to-End (E2E) systems often use data augmentation methods to create child-like speech from adults’ speech. For adult speech-trained ASRs, we investigate the effectiveness of augmentation methods; speed perturbations and spectral augmentation, along with VTLN, in an E2E framework for the CSR task, comparing these across Dutch, German, and Mandarin. We applied VTLN at different stages (training/test) of the ASR and conducted age and gender analyses. Our experiments showed highly similar patterns across the languages: Speed Perturbations and Spectral Augmentation yield significant performance improvements, while VTLN provided further improvements while maintaining recognition performance on adults’ speech (depending on when it is applied). Additionally, VTLN showed performance improvement for both male and female speakers and was particularly effective for younger children.

Noise robust in-domain children speech enhancement for automatic Punjabi recognition system under mismatched conditions

This work investigates the use of subglottal resonances (SGRs) for speaker normalization in noisy environments. Based on our previous work, a noise-robust algorithm is developed for estimating the first three SGRs from speech signals; it achieves robustness by factoring the short-term (or local) signal-to-noise ratio into the estimation process. The SGR estimates provided by this algorithm are refined by applying maximum-likelihood (ML) corrections, and are used in a non-linear frequency-warping technique that we recently developed. This SGR-based normalization (SN) scheme is evaluated on the AURORA-4 database in clean and noisy conditions. Using power-normalized cepstral coefficients (PNCCs) as front-end features, SN reduces the average word error rate by 8.7% relative to ML-based vocal-tract length normalization (VTLN). A fast version of SN (without ML corrections of SGR estimates) is also found to outperform VTLN (by 5.9% relative); it is computationally less complex than VTLN and hence a potential alternative for real-time applications.

Vocal Tract Length Normalization (VTLN) is used to design vocal tract length normalized Automatic Speech Recognition (ASR) systems. It has led to improvement in the performance of ASR systems by taking into account the physiological differences among speakers. Recently, a number of speech recognition applications are being developed for Indian languages. In this paper, we use state-of-the-art method for VTLN based on maximum likelihood approach. A vowel recognition system has been developed for two low resourced Indian languages, viz., Gujarati and Marathi. Appropriate warping factors have been obtained for all speakers considered for training and testing procedures. An improvement in the performance of vowel recognition is observed as compared to state-of-the-art Mel Frequency Cepstral Coefficients (MFCC).

The performance of automatic speech recognition systems for children’s speech is known to suffer from the large variation and mismatch in the acoustic and linguistic attributes between children’s and adults’ speech. One of the various identified sources of mismatch is the difference in formant frequencies between adults and children. In this paper, we propose a formant modification method to mitigate differences between adults’ and children’s speech and to improve the performance of ASR for children. The explored technique gives a relative 27% improvement in system performance compared to a hybrid DNN-HMM baseline. We also compare the system performance with related speaker adaptation methods like vocal tract length normalization (VTLN) and speaking rate adaptation (SRA) and find that the proposed method gives improvements over them, as well. Combining the proposed method with VTLN and SRA results in a further reduction of WER. We also found that the proposed method performs well even for noisy speech.

The automatic recognition of children's speech in acoustically mismatched conditions is a challenging problem on account of large difference in adults' and children's speech. In literature, this challenge is often addressed through concatenation of various feature/model domain adaptation methods like vocal tract length normalization (VTLN), maximum likelihood linear regression (MLLR) and heteroscedastic linear discriminant analysis (HLDA). But a significant gap in the performance of adults and children still remains. This work explores the eigenvoices (EV) based adaptation for addressing the gap in recognition performance of children's speech on adults' speech trained acoustic models. EV is a fast adaptation approach and helps in an effective gender biasing of the acoustic models. On combining EV with VTLN, MLLR and HLDA, under mismatched condition an absolute improvement of about 50% over the unadapted speaker independent system performance is obtained and thus significantly reducing the gap between the performances for adults and children.

We have developed and evaluated a set of speaker normalization procedures derived by bilinear transform (BLT) to compensate for variations in vocal tract lengths of different classes of speakers. The warping factors are estimated using the average third formants and their bandwidth, leaving out the exhaustive search. The MFCC of the testing data are transformed by the warped Mel filterbanks to match the models of the training data. The effectiveness of this set of speaker normalization procedures is examined in an experimental study performed using an isolated digit database of man, woman and children comparing to other standard speaker normalization method. The results of experiments demonstrate their capacity to achieve recognition accuracy increase of 19.5% and 16.5% at the best.

Vocal tract length normalization (VTLN) is a process of reducing the effects of VTL differences among speakers. In this paper, frequency warping based on Bark scale is presented to obtain VTL normalized features and Bark scale based features (BSBF) have been proposed. BSBFs are found to improve the accuracy of vowel recognition task by 1.05 % over MFCCs. A feature-level fusion of BSBFs with MFCCs performs better than MFCCs alone under matched and mismatched conditions in phoneme recognition.

Contextual invariant-integration features for improved speaker-independent speech recognition

Speech signal preprocessing is the first and the most important step in the automatic speech recognition process. The preprocessing of speech consists of cleaning the speech signal from ambient and undesirable noises, detecting speech activity, and normalizing the length of the vocal tract. The objective of preprocessing a speech signal is to make the speech recognition systems computationally more efficient through the application of several preprocessing techniques, such as speech pre-emphasis, vocal tract length normalization, voice activity detection, noise removal, framing, and windowing. This paper gives an overview of the fundamentals of speech signal preprocessing techniques, by highlighting the specifics and the requirements of each technique. We also explore all aspects that can improve the results of each technique. We aim that the content of this paper will help researchers improve the quality of their speech recognition systems by identifying appropriate speech preprocessing techniques to use in their experimental settings.

This paper describes a number of recent improvements to the HTK Broadcast News Transcription System. Changes to the system include the use of more acoustic training data; use of cluster-based variance normalisation and vocal tract length normalisation; the use of interpolated language models and enhanced adaptation using a full variance transform. These changes produce an reduction in word error rate of 13%. A simpli ed version of the system has also been constructed that runs in less than 10 times real-time and gives a 2.3% absolute higher error rate than the 300xRT full system.

We study the effect of different feature space normalization techniques in adverse acoustic conditions. Recognition tests are reported for cepstral mean and variance normalization, histogram normalization, feature space rotation, and vocal tract length normalization on a German isolated word recognition task with large acoustic mismatch. The training data was recorded in a clean office environment and the test data in cars. Speech recognition failed completely without normalization on the highway dataset, whereas the word error rate could be reduced to 17% using an online setup and to 10% with an offline setup.