Jani Nurminen

Tampere University

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Using robust viterbi algorithm and HMM-modeling in unit selection TTS to replace units of poor quality

Interspeech 2022 (2010)

Hanna Silén Elina Helander Jani Nurminen Konsta Koppinen Moncef Gabbouj

In hidden Markov model-based unit selection synthesis, the benefits of both unit selection and statistical parametric speech synthesis are combined. However, conventional Viterbi algorithm is forced to do a selection also when no suitable units are available. This can drift the search and decrease the overall quality. Consequently, we propose to use robust Viterbi algorithm that can simultaneously detect bad units and select the best sequence. The unsuitable units are replaced using hidden Markov model-based synthesis. Evaluations indicate that the use of robust Viterbi algorithm combined with unit replacement increases the quality compared to the traditional algorithm. Index Terms: robust Viterbi algorithm, unit selection, hidden Markov models

Viterbi algorithm

Forward algorithm

Iterative Viterbi decoding

Selection algorithm

10.21437/interspeech.2010-76

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Citations (7)

Speech Enhancement, Modeling and Recognition- Algorithms and Applications

InTech eBooks (2012)

Rudy Rotili Emanuele Principi Stefano Squartini Francesco Piazza Abdelaziz Trabelsi

This book on Speech Processing consists of seven chapters written by eminent researchers from Italy, Canada, India, Tunisia, Finland and The Netherlands. The chapters covers important fields in speech processing such as speech enhancement, noise cancellation, multi resolution spectral analysis, voice conversion, speech recognition and emotion recognition from speech. The chapters contain both survey and original research materials in addition to applications. This book will be useful to graduate students, researchers and practicing engineers working in speech processing.

10.5772/2391

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Citations (26)

Adding Value to Message Mediation in Multioperator MMS Network Architecture

Jani Nurminen

Value (mathematics)

Source

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Multi-mode matrix quantizer for low bit rate LSF quantization

Ulpu Sinervo Jani Nurminen Ari Heikkinen Jukka Saarinen

10.21437/eurospeech.2003-355

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Citations (3)

A Study of Bilinear Models in Voice Conversion

Journal of Signal and Information Processing (2011)

Victor Popa Jani Nurminen Moncef Gabbouj

This paper presents a voice conversion technique based on bilinear models and introduces the concept of contextual modeling. The bilinear approach reformulates the spectral envelope representation from line spectral frequencies feature to a two-factor parameterization corresponding to speaker identity and phonetic information, the so-called style and content factors. This decomposition offers a flexible representation suitable for voice conversion and facilitates the use of efficient training algorithms based on singular value decomposition. In a contextual approach (bilinear) models are trained on subsets of the training data selected on the fly at conversion time depending on the characteristics of the feature vector to be converted. The performance of bilinear models and context modeling is evaluated in objective and perceptual tests by comparison with the popular GMM-based voice conversion method for several sizes and different types of training data.

Representation

Feature (linguistics)

10.4236/jsip.2011.22017

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Citations (8)

Ways to implement global variance in statistical speech synthesis

Interspeech 2022 (2012)

Hanna Silén Elina Helander Jani Nurminen Moncef Gabbouj

Hidden Markov model-based speech synthesis is prone to over-smoothing of spectral parameter trajectories. The maximum-likelihood parameter generation favors smooth tracks and the utterance-level variance of each parameter trajectory is significantly reduced compared to the original recordings. This results in muffled speech. To retain the natural variance, statistical global variance modeling has been used in parameter generation. The modeling increases the utterancelevel variance in synthesis, but it is computationally demanding: there is no closed-form solution and an iterative approach is used. In this paper, we analyze the performance of two simple alternative approaches for retaining the natural variance of spectral parameters in synthesis, namely variance scaling and histogram equalization. Both methods apply analytically solvable parameter generation and impose the natural variance afterwards as an efficient post-processing step. Subjective evaluations carried out on English data confirm that the achieved synthesis quality is higher compared to simple post-filtering and similar to the standard global variance modeling.

Smoothing

10.21437/interspeech.2012-367

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Citations (34)

Evaluation of detailed modeling of the LP residual in statistical speech synthesis

2022 IEEE International Symposium on Circuits and Systems (ISCAS) (2013)

Jani Nurminen Hanna Silén Elina Helander Moncef Gabbouj

Speech parameterization remains an open question in statistical speech synthesis. In our earlier work we have shown that a framework developed originally for highly efficient speech storage can also be successfully applied for voice conversion and concatenative unit selection based speech synthesis. Recently, we have also used the same coding scheme in hybrid-form speech synthesis. In this paper, we further discuss the framework and apply it in statistical speech synthesis, concentrating specifically on the spectral modeling of the linear prediction (LP) residual. Perceptual evaluation demonstrates that the modeling of the spectral details remaining in the residual improves the quality of synthetic speech.

Linear prediction

10.1109/iscas.2013.6571844

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Citations (5)

Voice Conversion Using Partial Least Squares Regression

IEEE Transactions on Audio Speech and Language Processing (2010)

Elina Helander Tuomas Virtanen Jani Nurminen Moncef Gabbouj

Voice conversion can be formulated as finding a mapping function which transforms the features of the source speaker to those of the target speaker. Gaussian mixture model (GMM)-based conversion is commonly used, but it is subject to overfitting. In this paper, we propose to use partial least squares (PLS)-based transforms in voice conversion. To prevent overfitting, the degrees of freedom in the mapping can be controlled by choosing a suitable number of components. We propose a technique to combine PLS with GMMs, enabling the use of multiple local linear mappings. To further improve the perceptual quality of the mapping where rapid transitions between GMM components produce audible artefacts, we propose to low-pass filter the component posterior probabilities. The conducted experiments show that the proposed technique results in better subjective and objective quality than the baseline joint density GMM approach. In speech quality conversion preference tests, the proposed method achieved 67% preference score against the smoothed joint density GMM method and 84% preference score against the unsmoothed joint density GMM method. In objective tests the proposed method produced a lower Mel-cepstral distortion than the reference methods.

Overfitting

Mel-frequency cepstrum

Distortion (music)

10.1109/tasl.2010.2041699

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Citations (217)

Efficient technique for quantization of pitch contours

Speech prosody (2006)

Jani Nurminen Sakari Himanen Anssi Rämö

This paper introduces an efficient technique for pitch contour quantization designed mainly for applications that require storage of speech or prosodic information at a high compression ratio.Instead of quantizing the estimated pitch values directly, the proposed technique forms and quantizes a simplified model of the pitch contour.The simplified contour is constructed in such a manner that the amount of information needed for describing it is minimized.At the same time, the deviation from the original contour is maintained below a predetermined limit.In addition to the high compression ratio, the contour representation offers benefits in pitch-synchronous decoding.The proposed technique is implemented and evaluated in a practical storage speech coder.According to the evaluation, the performance of the quantization technique is very promising as it achieves perceptually satisfactory quality at an average bit rate of about 100 bits per second.

Pitch contour

Pitch detection algorithm

10.21437/speechprosody.2006-127

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Optimization of text database using hierachical clustering

IEEE International Conference on Acoustics Speech and Signal Processing (2009)

Jilei Tian Jani Nurminen

Many speech and language related techniques employ models that are trained using text data. In this paper, we introduce a novel method for selecting optimized training sets from text databases. The coverage of the subset selected for training is optimized using hierarchical clustering and the generalized Levenshtein distance. The validity of the proposed subset optimization technique is verified in a data-driven syllabification task. The results clearly indicate that the proposed approach meaningfully optimizes the training set, which in turn improves the quality of the trained model. Compared to the existing state-of-the-art data selection technique, the proposed hierarchical clustering approach improves the compactness of data clusters, decreases the computational complexity and makes data set selection scalable. The presented idea can be used in a wide variety of language processing applications that require training with text data.

Levenshtein distance

Hierarchical clustering

10.1109/icassp.2009.4960572

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Citations (3)