26 research outputs found

    Models and analysis of vocal emissions for biomedical applications: 5th International Workshop: December 13-15, 2007, Firenze, Italy

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    The MAVEBA Workshop proceedings, held on a biannual basis, collect the scientific papers presented both as oral and poster contributions, during the conference. The main subjects are: development of theoretical and mechanical models as an aid to the study of main phonatory dysfunctions, as well as the biomedical engineering methods for the analysis of voice signals and images, as a support to clinical diagnosis and classification of vocal pathologies. The Workshop has the sponsorship of: Ente Cassa Risparmio di Firenze, COST Action 2103, Biomedical Signal Processing and Control Journal (Elsevier Eds.), IEEE Biomedical Engineering Soc. Special Issues of International Journals have been, and will be, published, collecting selected papers from the conference

    Pre-processing of Speech Signals for Robust Parameter Estimation

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    ESCOM 2017 Book of Abstracts

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    A Parametric Approach for Efficient Speech Storage, Flexible Synthesis and Voice Conversion

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    During the past decades, many areas of speech processing have benefited from the vast increases in the available memory sizes and processing power. For example, speech recognizers can be trained with enormous speech databases and high-quality speech synthesizers can generate new speech sentences by concatenating speech units retrieved from a large inventory of speech data. However, even in today's world of ever-increasing memory sizes and computational resources, there are still lots of embedded application scenarios for speech processing techniques where the memory capacities and the processor speeds are very limited. Thus, there is still a clear demand for solutions that can operate with limited resources, e.g., on low-end mobile devices. This thesis introduces a new segmental parametric speech codec referred to as the VLBR codec. The novel proprietary sinusoidal speech codec designed for efficient speech storage is capable of achieving relatively good speech quality at compression ratios beyond the ones offered by the standardized speech coding solutions, i.e., at bitrates of approximately 1 kbps and below. The efficiency of the proposed coding approach is based on model simplifications, mode-based segmental processing, and the method of adaptive downsampling and quantization. The coding efficiency is also further improved using a novel flexible multi-mode matrix quantizer structure and enhanced dynamic codebook reordering. The compression is also facilitated using a new perceptual irrelevancy removal method. The VLBR codec is also applied to text-to-speech synthesis. In particular, the codec is utilized for the compression of unit selection databases and for the parametric concatenation of speech units. It is also shown that the efficiency of the database compression can be further enhanced using speaker-specific retraining of the codec. Moreover, the computational load is significantly decreased using a new compression-motivated scheme for very fast and memory-efficient calculation of concatenation costs, based on techniques and implementations used in the VLBR codec. Finally, the VLBR codec and the related speech synthesis techniques are complemented with voice conversion methods that allow modifying the perceived speaker identity which in turn enables, e.g., cost-efficient creation of new text-to-speech voices. The VLBR-based voice conversion system combines compression with the popular Gaussian mixture model based conversion approach. Furthermore, a novel method is proposed for converting the prosodic aspects of speech. The performance of the VLBR-based voice conversion system is also enhanced using a new approach for mode selection and through explicit control of the degree of voicing. The solutions proposed in the thesis together form a complete system that can be utilized in different ways and configurations. The VLBR codec itself can be utilized, e.g., for efficient compression of audio books, and the speech synthesis related methods can be used for reducing the footprint and the computational load of concatenative text-to-speech synthesizers to levels required in some embedded applications. The VLBR-based voice conversion techniques can be used to complement the codec both in storage applications and in connection with speech synthesis. It is also possible to only utilize the voice conversion functionality, e.g., in games or other entertainment applications

    Voice source characterization for prosodic and spectral manipulation

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    The objective of this dissertation is to study and develop techniques to decompose the speech signal into its two main components: voice source and vocal tract. Our main efforts are on the glottal pulse analysis and characterization. We want to explore the utility of this model in different areas of speech processing: speech synthesis, voice conversion or emotion detection among others. Thus, we will study different techniques for prosodic and spectral manipulation. One of our requirements is that the methods should be robust enough to work with the large databases typical of speech synthesis. We use a speech production model in which the glottal flow produced by the vibrating vocal folds goes through the vocal (and nasal) tract cavities and its radiated by the lips. Removing the effect of the vocal tract from the speech signal to obtain the glottal pulse is known as inverse filtering. We use a parametric model fo the glottal pulse directly in the source-filter decomposition phase. In order to validate the accuracy of the parametrization algorithm, we designed a synthetic corpus using LF glottal parameters reported in the literature, complemented with our own results from the vowel database. The results show that our method gives satisfactory results in a wide range of glottal configurations and at different levels of SNR. Our method using the whitened residual compared favorably to this reference, achieving high quality ratings (Good-Excellent). Our full parametrized system scored lower than the other two ranking in third place, but still higher than the acceptance threshold (Fair-Good). Next we proposed two methods for prosody modification, one for each of the residual representations explained above. The first method used our full parametrization system and frame interpolation to perform the desired changes in pitch and duration. The second method used resampling on the residual waveform and a frame selection technique to generate a new sequence of frames to be synthesized. The results showed that both methods are rated similarly (Fair-Good) and that more work is needed in order to achieve quality levels similar to the reference methods. As part of this dissertation, we have studied the application of our models in three different areas: voice conversion, voice quality analysis and emotion recognition. We have included our speech production model in a reference voice conversion system, to evaluate the impact of our parametrization in this task. The results showed that the evaluators preferred our method over the original one, rating it with a higher score in the MOS scale. To study the voice quality, we recorded a small database consisting of isolated, sustained Spanish vowels in four different phonations (modal, rough, creaky and falsetto) and were later also used in our study of voice quality. Comparing the results with those reported in the literature, we found them to generally agree with previous findings. Some differences existed, but they could be attributed to the difficulties in comparing voice qualities produced by different speakers. At the same time we conducted experiments in the field of voice quality identification, with very good results. We have also evaluated the performance of an automatic emotion classifier based on GMM using glottal measures. For each emotion, we have trained an specific model using different features, comparing our parametrization to a baseline system using spectral and prosodic characteristics. The results of the test were very satisfactory, showing a relative error reduction of more than 20% with respect to the baseline system. The accuracy of the different emotions detection was also high, improving the results of previously reported works using the same database. Overall, we can conclude that the glottal source parameters extracted using our algorithm have a positive impact in the field of automatic emotion classification

    HMM-based speech synthesis using an acoustic glottal source model

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    Parametric speech synthesis has received increased attention in recent years following the development of statistical HMM-based speech synthesis. However, the speech produced using this method still does not sound as natural as human speech and there is limited parametric flexibility to replicate voice quality aspects, such as breathiness. The hypothesis of this thesis is that speech naturalness and voice quality can be more accurately replicated by a HMM-based speech synthesiser using an acoustic glottal source model, the Liljencrants-Fant (LF) model, to represent the source component of speech instead of the traditional impulse train. Two different analysis-synthesis methods were developed during this thesis, in order to integrate the LF-model into a baseline HMM-based speech synthesiser, which is based on the popular HTS system and uses the STRAIGHT vocoder. The first method, which is called Glottal Post-Filtering (GPF), consists of passing a chosen LF-model signal through a glottal post-filter to obtain the source signal and then generating speech, by passing this source signal through the spectral envelope filter. The system which uses the GPF method (HTS-GPF system) is similar to the baseline system, but it uses a different source signal instead of the impulse train used by STRAIGHT. The second method, called Glottal Spectral Separation (GSS), generates speech by passing the LF-model signal through the vocal tract filter. The major advantage of the synthesiser which incorporates the GSS method, named HTS-LF, is that the acoustic properties of the LF-model parameters are automatically learnt by the HMMs. In this thesis, an initial perceptual experiment was conducted to compare the LFmodel to the impulse train. The results showed that the LF-model was significantly better, both in terms of speech naturalness and replication of two basic voice qualities (breathy and tense). In a second perceptual evaluation, the HTS-LF system was better than the baseline system, although the difference between the two had been expected to be more significant. A third experiment was conducted to evaluate the HTS-GPF system and an improved HTS-LF system, in terms of speech naturalness, voice similarity and intelligibility. The results showed that the HTS-GPF system performed similarly to the baseline. However, the HTS-LF system was significantly outperformed by the baseline. Finally, acoustic measurements were performed on the synthetic speech to investigate the speech distortion in the HTS-LF system. The results indicated that a problem in replicating the rapid variations of the vocal tract filter parameters at transitions between voiced and unvoiced sounds is the most significant cause of speech distortion. This problem encourages future work to further improve the system

    Observations on the dynamic control of an articulatory synthesizer using speech production data

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    This dissertation explores the automatic generation of gestural score based control structures for a three-dimensional articulatory speech synthesizer. The gestural scores are optimized in an articulatory resynthesis paradigm using a dynamic programming algorithm and a cost function which measures the deviation from a gold standard in the form of natural speech production data. This data had been recorded using electromagnetic articulography, from the same speaker to which the synthesizer\u27s vocal tract model had previously been adapted. Future work to create an English voice for the synthesizer and integrate it into a text-to-speech platform is outlined.Die vorliegende Dissertation untersucht die automatische Erzeugung von gesturalpartiturbasierten Steuerdaten für ein dreidimensionales artikulatorisches Sprachsynthesesystem. Die gesturalen Partituren werden in einem artikulatorischen Resynthese-Paradigma mittels dynamischer Programmierung optimiert, unter Zuhilfenahme einer Kostenfunktion, die den Abstand zu einem "Gold Standard" in Form natürlicher Sprachproduktionsdaten mißt. Diese Daten waren mit elektromagnetischer Artikulographie am selben Sprecher aufgenommen worden, an den zuvor das Vokaltraktmodell des Synthesesystems angepaßt worden war. Weiterführende Forschung, eine englische Stimme für das Synthesesystem zu erzeugen und sie in eine Text-to-Speech-Plattform einzubetten, wird umrissen

    Personalising synthetic voices for individuals with severe speech impairment.

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    Speech technology can help individuals with speech disorders to interact more easily. Many individuals with severe speech impairment, due to conditions such as Parkinson's disease or motor neurone disease, use voice output communication aids (VOCAs), which have synthesised or pre-recorded voice output. This voice output effectively becomes the voice of the individual and should therefore represent the user accurately. Currently available personalisation of speech synthesis techniques require a large amount of data input, which is difficult to produce for individuals with severe speech impairment. These techniques also do not provide a solution for those individuals whose voices have begun to show the effects of dysarthria. The thesis shows that Hidden Markov Model (HMM)-based speech synthesis is a promising approach for 'voice banking' for individuals before their condition causes deterioration of the speech and once deterioration has begun. Data input requirements for building personalised voices with this technique using human listener judgement evaluation is investigated. It shows that 100 sentences is the minimum required to build a significantly different voice from an average voice model and show some resemblance to the target speaker. This amount depends on the speaker and the average model used. A neural network analysis trained on extracted acoustic features revealed that spectral features had the most influence for predicting human listener judgements of similarity of synthesised speech to a target speaker. Accuracy of prediction significantly improves if other acoustic features are introduced and combined non-linearly. These results were used to inform the reconstruction of personalised synthetic voices for speakers whose voices had begun to show the effects of their conditions. Using HMM-based synthesis, personalised synthetic voices were built using dysarthric speech showing similarity to target speakers without recreating the impairment in the synthesised speech output

    APCO project 25 wireless data services over land mobile radio channel for smaller law enforcement agencies

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    Digital data messages are very important in modern communication systems and advanced mobile data technologies have opened the door to a wide range of applications and services in the public safety environment. Still, the availability of mobile data services among public safety agencies is hampered by two issues of the implementation of data communication: the reliability of commercial data services and the high cost of the equipment needed to support mixed voice and data transmissions over private land mobile radio channels. This thesis describes the design and development of an inexpensive Software Defined APCO Project 25 Data Base Station that allows smaller law enforcement agencies to enable data services in their cruisers in a cost effective way. The data base station is comprised of a standard PC interfaced to a commercial analog VHF FM transceiver via a commercial PC sound card. The base station is compliant with commercial P25 digital mobile radios and operates in parallel to commercial P25 digital voice communications equipment
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