Speaker normalisation for large vocabulary multiparty conversational speech recognition

One of the main problems faced by automatic speech recognition is the variability of the testing conditions. This is due both to the acoustic conditions (different transmission channels, recording devices, noises etc.) and to the variability of speech across different speakers (i.e. due to different accents, coarticulation of phonemes and different vocal tract characteristics). Vocal tract length normalisation (VTLN) aims at normalising the acoustic signal, making it independent from the vocal tract length. This is done by a speaker specific warping of the frequency axis parameterised through a warping factor. In this thesis the application of VTLN to multiparty conversational speech was investigated focusing on the meeting domain. This is a challenging task showing a great variability of the speech acoustics both across different speakers and across time for a given speaker. VTL, the distance between the lips and the glottis, varies over time. We observed that the warping factors estimated using Maximum Likelihood seem to be context dependent: appearing to be influenced by the current conversational partner and being correlated with the behaviour of formant positions and the pitch. This is because VTL also influences the frequency of vibration of the vocal cords and thus the pitch. In this thesis we also investigated pitch-adaptive acoustic features with the goal of further improving the speaker normalisation provided by VTLN. We explored the use of acoustic features obtained using a pitch-adaptive analysis in combination with conventional features such as Mel frequency cepstral coefficients. These spectral representations were combined both at the acoustic feature level using heteroscedastic linear discriminant analysis (HLDA), and at the system level using ROVER. We evaluated this approach on a challenging large vocabulary speech recognition task: multiparty meeting transcription. We found that VTLN benefits the most from pitch-adaptive features. Our experiments also suggested that combining conventional and pitch-adaptive acoustic features using HLDA results in a consistent, significant decrease in the word error rate across all the tasks. Combining at the system level using ROVER resulted in a further significant improvement. Further experiments compared the use of pitch adaptive spectral representation with the adoption of a smoothed spectrogram for the extraction of cepstral coefficients. It was found that pitch adaptive spectral analysis, providing a representation which is less affected by pitch artefacts (especially for high pitched speakers), delivers features with an improved speaker independence. Furthermore this has also shown to be advantageous when HLDA is applied. The combination of a pitch adaptive spectral representation and VTLN based speaker normalisation in the context of LVCSR for multiparty conversational speech led to more speaker independent acoustic models improving the overall recognition performances

Vocal Tract Length Normalization for Statistical Parametric Speech Synthesis

Vocal tract length normalization (VTLN) has been successfully used in automatic speech recognition for improved performance. The same technique can be implemented in statistical parametric speech synthesis for rapid speaker adaptation during synthesis. This paper presents an efficient implementation of VTLN using expectation maximization and addresses the key challenges faced in implementing VTLN for synthesis. Jacobian normalization, high dimensionality features and truncation of the transformation matrix are a few challenges presented with the appropriate solutions. Detailed evaluations are performed to estimate the most suitable technique for using VTLN in speech synthesis. Evaluating VTLN in the framework of speech synthesis is also not an easy task since the technique does not work equally well for all speakers. Speakers have been selected based on different objective and subjective criteria to demonstrate the difference between systems. The best method for implementing VTLN is confirmed to be use of the lower order features for estimating warping factors

Acoustic variability and automatic recognition of children’s speech

International audienc

Adaptation of children’s speech with limited data based on formant-like peak alignment,”

Abstract Automatic recognition of children's speech using acoustic models trained by adults results in poor performance due to differences in speech acoustics. These acoustical differences are a consequence of children having shorter vocal tracts and smaller vocal cords than adults. Hence, speaker adaptation needs to be performed. However, in real-world applications, the amount of adaptation data available may be less than what is needed by common speaker adaptation techniques to yield reasonable performance. In this paper, we first study, in the discrete frequency domain, the relationship between frequency warping in the front-end and corresponding transformations in the back-end. Three common feature extraction schemes are investigated and their transformation linearity in the back-end are discussed. In particular, we show that under certain approximations, frequency warping of MFCC features with Mel-warped triangular filter banks equals a linear transformation in the cepstral space. Based on that linear transformation, a formant-like peak alignment algorithm is proposed to adapt adult acoustic models to children's speech. The peaks are estimated by Gaussian mixtures using the Expectation-Maximization (EM) algorith

Combining Vocal Tract Length Normalization with Linear Transformations in a Bayesian Framework

Recent research has demonstrated the effectiveness of vocal tract length normalization (VTLN) as a rapid adaptation technique for statistical parametric speech synthesis. VTLN produces speech with naturalness preferable to that of MLLR- based adaptation techniques, being much closer in quality to that generated by the original average voice model. By contrast, with just a single parameter, VTLN captures very few speaker specific characteristics when compared to the available linear transform based adaptation techniques. This paper proposes that the merits of VTLN can be combined with those of linear transform based adaptation technique in a Bayesian framework, where VTLN is used as the prior information. A novel technique of propa- gating the gender information from the VTLN prior through constrained structural maximum a posteriori linear regression (CSMAPLR) adaptation is presented. Experiments show that the resulting transformation has improved speech quality with better naturalness, intelligibility and improved speaker similarity

Study of Jacobian Normalization for VTLN

The divergence of the theory and practice of vocal tract length normalization (VTLN) is addressed, with particular emphasis on the role of the Jacobian determinant. VTLN is placed in a Bayesian setting, which brings in the concept of a prior on the warping factor. The form of the prior, together with acoustic scaling and numerical conditioning are then discussed and evaluated. It is concluded that the Jacobian determinant is important in VTLN, especially for the high dimensional features used in HMM based speech synthesis, and difficulties normally associated with the Jacobian determinant can be attributed to prior and scaling

Bias Adaptation for Vocal Tract Length Normalization

Vocal tract length normalisation (VTLN) is a well known rapid adaptation technique. VTLN as a linear transformation in the cepstral domain results in the scaling and translation factors. The warping factor represents the spectral scaling parameter. While, the translation factor represented by bias term captures more speaker characteristics especially in a rapid adaptation framework without having the risk of over-fitting. This paper presents a complete and comprehensible derivation of the bias transformation for VTLN and implements it in a unified framework for statistical parametric speech synthesis and recognition. The recognition experiments show that bias term improves the rapid adaptation performance and gives additional performance over the cepstral mean normalisation factor. It was observed from the synthesis results that VTLN bias term did not have much effect in combination with model adaptation techniques that already have a bias transformation incorporated

Current trends in multilingual speech processing

In this paper, we describe recent work at Idiap Research Institute in the domain of multilingual speech processing and provide some insights into emerging challenges for the research community. Multilingual speech processing has been a topic of ongoing interest to the research community for many years and the field is now receiving renewed interest owing to two strong driving forces. Firstly, technical advances in speech recognition and synthesis are posing new challenges and opportunities to researchers. For example, discriminative features are seeing wide application by the speech recognition community, but additional issues arise when using such features in a multilingual setting. Another example is the apparent convergence of speech recognition and speech synthesis technologies in the form of statistical parametric methodologies. This convergence enables the investigation of new approaches to unified modelling for automatic speech recognition and text-to-speech synthesis (TTS) as well as cross-lingual speaker adaptation for TTS. The second driving force is the impetus being provided by both government and industry for technologies to help break down domestic and international language barriers, these also being barriers to the expansion of policy and commerce. Speech-to-speech and speech-to-text translation are thus emerging as key technologies at the heart of which lies multilingual speech processin