Extraction of vocal-tract system characteristics from speechsignals

We propose methods to track natural variations in the characteristics of the vocal-tract system from speech signals. We are especially interested in the cases where these characteristics vary over time, as happens in dynamic sounds such as consonant-vowel transitions. We show that the selection of appropriate analysis segments is crucial in these methods, and we propose a selection based on estimated instants of significant excitation. These instants are obtained by a method based on the average group-delay property of minimum-phase signals. In voiced speech, they correspond to the instants of glottal closure. The vocal-tract system is characterized by its formant parameters, which are extracted from the analysis segments. Because the segments are always at the same relative position in each pitch period, in voiced speech the extracted formants are consistent across successive pitch periods. We demonstrate the results of the analysis for several difficult cases of speech signals

On timing in time-frequency analysis of speech signals

The objective of this paper is to demonstrate the importance of position of the analysis time window in time-frequency analysis of speech signals. Speech signals contain information about the time varying characteristics of the excitation source and the vocal tract system. Resolution in both the temporal and spectral domains is essential for extracting the source and system characteristics from speech signals. It is not only the resolution, as determined by the analysis window in the time domain, but also the position of the window with respect to the production characteristics that is important for accurate analysis of speech signals. In this context, we propose an event-based approach for speech signals. We define the occurrence of events at the instants corresponding to significant excitation of the vocal tract system. Knowledge of these instants enable us to place the analysis window suitably for extracting the characteristics of the excitation source and the vocal tract system even from short segments of data. We present a method of extracting the instants of significant excitation from speech signals. We show that with the knowledge of these instants it is possible to perform prosodic manipulation of speech and also an accurate analysis of speech for extracting the source and system characteristics

The use of spectral information in the development of novel techniques for speech-based cognitive load classification

The cognitive load of a user refers to the amount of mental demand imposed on the user when performing a particular task. Estimating the cognitive load (CL) level of the users is necessary to adjust the workload imposed on them accordingly in order to improve task performance. The current speech based CL classification systems are not adequate for commercial use due to their low performance particularly in noisy environments. This thesis proposes many techniques to improve the performance of the speech based cognitive load classification system in both clean and noisy conditions. This thesis analyses and presents the effectiveness of speech features such as spectral centroid frequency (SCF) and spectral centroid amplitude (SCA) for CL classification. Sub-systems based on SCF and SCA features were developed and fused with the traditional Mel frequency cepstral coefficients (MFCC) based system, producing an 8.9% and 31.5% relative error rate reduction respectively when compared to the MFCC-based system alone. The Stroop test corpus was used in these experiments. The investigation into cognitive load information in the form of spectral distribution in different subbands shows that the information distributed in the low frequency subband is significantly higher than the high frequency subband. Two different methods are proposed to utilize this finding. The first method, called the multi-band approach, uses a weighting scheme to emphasize the speech features in low frequency subbands. The cognitive load classification accuracy of this approach is shown to be higher than a system based on a non-weighting scheme. The second method is to design an effective filterbank based on the spectral distribution of cognitive load information using the Kullback-Leibler distance measure. It is shown that the designed filterbank consistently provides higher classification accuracies than other existing filterbanks such as mel, Bark, and equivalent rectangular bandwidth. A discrete cosine transform based speech enhancement technique is proposed in order to increase the robustness of the CL classification system and found to be more suitable than other methods investigated. This proposed method provides a 3.0% average relative error rate reduction for the seven types of noise and five levels of SNR used. In particular, it provides a maximum of 7.5% relative error rate reduction for the F16 noise (in NOISEX-92 database) at 20 dB SNR

A real-time audio spectral analyser using active filters with adjustable parameters

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Voice source characterization for prosodic and spectral manipulation

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

Analysis, Vocal-tract modeling, and Automatic Detection of Vowel Nasalization

The aim of this work is to clearly understand the salient features of nasalization and the sources of acoustic variability in nasalized vowels, and to suggest Acoustic Parameters (APs) for the automatic detection of vowel nasalization based on this knowledge. Possible applications in automatic speech recognition, speech enhancement, speaker recognition and clinical assessment of nasal speech quality have made the detection of vowel nasalization an important problem to study. Although several researchers in the past have found a number of acoustical and perceptual correlates of nasality, automatically extractable APs that work well in a speaker-independent manner are yet to be found. In this study, vocal tract area functions for one American English speaker, recorded using Magnetic Resonance Imaging, were used to simulate and analyze the acoustics of vowel nasalization, and to understand the variability due to velar coupling area, asymmetry of nasal passages, and the paranasal sinuses. Based on this understanding and an extensive survey of past literature, several automatically extractable APs were proposed to distinguish between oral and nasalized vowels. Nine APs with the best discrimination capability were selected from this set through Analysis of Variance. The performance of these APs was tested on several databases with different sampling rates, recording conditions and languages. Accuracies of 96.28%, 77.90% and 69.58% were obtained by using these APs on StoryDB, TIMIT and WS96/97 databases, respectively, in a Support Vector Machine classifier framework. To my knowledge, these results are the best anyone has achieved on this task. These APs were also tested in a cross-language task to distinguish between oral and nasalized vowels in Hindi. An overall accuracy of 63.72% was obtained on this task. Further, the accuracy for phonemically nasalized vowels, 73.40%, was found to be much higher than the accuracy of 53.48% for coarticulatorily nasalized vowels. This result suggests not only that the same APs can be used to capture both phonemic and coarticulatory nasalization, but also that the duration of nasalization is much longer when vowels are phonemically nasalized. This language and category independence is very encouraging since it shows that these APs are really capturing relevant information

Computer speech synthesis: a systematic method to extract synthesis parameters for formant synthesizers.

by Yu Wai Leung.Thesis (M.Phil.)--Chinese University of Hong Kong, 1993.Includes bibliographical references (leaves 94-96).Abstract --- p.1Introduction --- p.2Chapter 1. --- Human speech and its production modelChapter 1.1 --- The human vocal system --- p.4Chapter 1.2 --- Speech production mechanism --- p.5Chapter 1.3 --- Acoustic properties of human speech --- p.5Chapter 1.4 --- Modeling the speech production process --- p.6Chapter 1.5 --- Speech as the spoken form of a language --- p.7Chapter 2. --- Speech analysis techniquesChapter 2.1 --- Short time speech analysis and speech segmentation --- p.9Chapter 2.2 --- Pre-emphasis --- p.9Chapter 2.3 --- Linear predictive analysis --- p.10Chapter 2.4 --- Formant tracking --- p.13Chapter 2.5 --- Pitch determination --- p.20Chapter 3. --- Speech synthesis technologyChapter 3.1 --- Overview --- p.24Chapter 3.2 --- Articulatory synthesis --- p.24Chapter 3.3 --- Concatenation synthesis --- p.24Chapter 3.4 --- LPC synthesis --- p.27Chapter 3.5 --- Formant speech synthesis --- p.28Chapter 3.6 --- Synthesis by rule --- p.29Chapter 4. --- LSYNTH: A parallel formant synthesizerChapter 4.1 --- OverviewChapter 4.2 --- Synthesizer configuration: cascade and parallel --- p.32Chapter 4.3 --- Structure ofLSYNTH --- p.33Chapter 5. --- Automatic formant parameter extraction for parallel formant synthesizersChapter 5.1 --- Introduction --- p.47Chapter 5.2 --- The idea of a feedback analysis system --- p.48Chapter 5.3 --- Overview of the feedback analysis system --- p.49Chapter 5.4 --- Iterative spectral matching algorithm --- p.52Chapter 5.5 --- Results and discussions --- p.65Chapter 6. --- Generate formant trajectories in synthesis-by-rule systemsChapter 6.1 --- Formant trajectories generation in synthesis-by-rule systems --- p.70Chapter 6.2 --- Modeling formant transitions --- p.71Chapter 6.3 --- Conventional formant transition calculation --- p.72Chapter 6.4 --- The 4-point Bezier curve model --- p.73Chapter 6.5 --- Modeling of formant transitions for Cantonese --- p.77Chapter 7. --- Some listening test resultsChapter 7.1 --- Introduction --- p.87Chapter 7.2 --- Tone recognition test --- p.87Chapter 7.3 --- Cantonese final recognition test --- p.89Chapter 7.4 --- Problems and discussions --- p.91Conclusion --- p.92References --- p.94Appendix A: The Cantonese phonetic system --- p.97"Appendix B: TPIT, A tone trajectory generator for Cantonese" --- p.10