Time-domain Pitch Synchronous Overlap Add Research Articles

Speech-Input Speech-Output Communication for Dysarthric Speakers Using HMM-Based Speech Recognition and Adaptive Synthesis System

Dysarthria is a motor speech disorder that causes inability to control and coordinate one or more articulators. This makes it difficult for a dysarthric speaker to utter certain speech sound units, thereby producing poorly articulated, slurred, and unintelligible speech. Hence, a speech supportive system needs to be developed to support them in their social difficulties. The current work aims at developing a speech supportive system, the objectives of which are threefold, namely (i) identifying the articulatory errors of each dysarthric speaker, (ii) developing a speech recognition system that corrects the errors in dysarthric speech by incorporating the findings from the first fold using a speaker-specific dictionary and (iii) developing an HMM-based speaker-adaptive speech synthesis system that synthesizes the error-corrected text for each dysarthric speaker retaining their identity. In the current work, the articulatory errors are analysed and identified, for 10 dysarthric speakers from the Nemours dysarthric speech corpus, using isolated-style phoneme recognition system trained with TIMIT speech corpus, followed by product of likelihood Gaussian-based analysis. The estimated articulatory errors are incorporated into a phoneme recognition system using speaker-specific dictionary and bigram language model. The error-corrected text is then synthesized as speech. The synthesized speech is evaluated to check its intelligibility and naturalness using mean opinion score. To further improve the intelligibility, speech rate of the synthesized speech is modified using time-domain pitch synchronous overlap add (TDPSOLA) technique. The results are quite encouraging, and this system is expected to be developed as a speech assistive device for a large vocabulary, in the near future, in a hand-held device.

Preliminary Studies towards Improving the Isolated Digit Recognition Performance of Dysarthric Speech by Prosodic Analysis

Abstract The objective of the present work is to improve the digit recognition performance of speech signals affected with dysarthria. The paper presents preliminary studies performed on universal access dysarthric speech recognition (UADSR) database. The works presented in the paper are organized into three stages. Firstly, the degradation in the digit recognition performance is demonstrated by testing the dysarthric digits with the acoustic models built using the digit samples spoken by controlled speakers. Secondly, the prosodic analysis is performed on the dysarthric isolated digits that are available in the database. Finally, the prosodic parameters of the dysarthric speech is manipulated to match with the normal speech which is used to build the acoustic models. Based on the experiments conducted, the manipulation of duration parameters using the state of the art time-domain pitch synchronous overlap add (TD-PSOLA) method observed to be significantly improving the recognition rates in contrast to other prosodic parameters. The improvement in the word recognition rates are also found to be in accordance with the intelligibility of the dysarthric speakers and hence proves the significance of using customized prosodic scaling factors according to the intelligibility levels of each of the subjects.

Research on the mechanism for phonating stressed English syllables based on DIVA model

Speech generation and acquisition is a complex cognitive process involving many brain regions. Simulating this process requires establishment of a proper neural network model according to the interaction between sensory organs and motor areas in the brain that occurs during phonation. Directions Into Velocities of Articulators (DIVA) is a neural network model of this type. In the process of speech generation and acquisition, this model can be used to control and simulate the trajectory of articulator and synthesize the desired speech. However, DIVA is flawed in terms of simulating natural language, because it cannot simulate the phonation of English stresses. A DIVA-based neural network computing model for analyzing stresses on English words is proposed. The TD-POSAL (time-domain pitch synchronous overlap add) algorithm is used to tune DIVA׳s speech and rhythm parameters in order to simulate the generation and acquisition of stresses on English words. Simulation showed that the improved DIVA model can represent the features of stressed syllables. The model׳s pitch and duration compensation effect is tested in the increased pitch perturbation experiment. The experiment demonstrates that the improved DIVA model can correctly simulate the generation and acquisition of stresses on English words.

A Hybrid Transformation and Filtering Approach for Speech Enhancement with Time Domain Pitch Synchronous Overlap-add

The main goal of Speech enhancement is to enhance the performance of speech communication systems in noisy environments. The problem of enhancing speech which is corrupted by noise is very large, although a lot of techniques have been introduced by the researchers over the past years. This problem is more severe when there is no additional information on the nature of noise degradation is available in which case the enhancement technique must utilize only the specific properties of the speech and noise signals. Signal representation and enhancement in cosine transformation is observed to provide significant results. Discrete Cosine Transformation has been widely used for speech enhancement. In this research study, instead of DCT, a hybrid technique called DCTSLT which is the combination of Discrete Cosine Transform (DCT) and Slantlet Transform (SLT) is proposed for continuous energy compaction along with critical sampling and flexible window switching. In order to deal with the issue of frame to frame deviations of the Cosine Transformations, the proposed transform is combined with Time Domain Pitch Synchronous Overlap-Add (TD-PSOLA) method. Moreover, in order to improve the performance of noise reduction of the system, a Hybrid Vector Wiener Filter approach (HVWF) is used in this study. Experimental result shows that the proposed system performs well in enhancing the speech as compared with other techniques.

Emotional Speech Synthesis Based on Prosodic Feature Modification

The synthesis of emotional speech has wide applications in the field of human-computer interaction, medicine, industry and so on. In this work, an emotional speech synthesis system is proposed based on prosodic features modification and Time Domain Pitch Synchronous OverLap Add (TD-PSOLA) waveform concatenative algorithm. The system produces synthesized speech with four types of emotion: angry, happy, sad and bored. The experiment results show that the proposed emotional speech synthesis system achieves a good performance. The produced utterances present clear emotional expression. The subjective test reaches high classification accuracy for different types of synthesized emotional speech utterances.

Pitch Detection of Speech Synthesis by Using Matlab

In speech synthesis, machine is developed which can accept text and convert into natural sounding speech. Applications of speech synthesis include speech output from computers, reading machine for the visually challenged people. The difference between text to speech synthesizer and any other talking machine (e.g., cassette player) is, it could be trained for any speaker's voice in a fully automatic way. Three main approaches to speech synthesis: articulator synthesis, formant synthesis, and concatenate synthesis. I am carrying out with concatenate synthesis approach. In addition, text-to-speech (TTS) conversion system based on time-domain pitch-synchronous overlap-add (TD-PSOLA) method, has been employed to perform prosody (includes pitch, duration of a speech) modification. 7 To assure good quality of synthetic speech accurate estimation of pitch-period and pitch-marks are necessary for pitch modification. Pitch marking is divided into two tasks; pitch detection and location determination. LPF and some nonlinearity are being used for pitch- detection; peak-valley decision method is used to determine the appropriate parts of speech for used in pitch- mark estimation. In each pitch period, two possible peaks/valleys are searched and one dynamic programming is run to obtain pitch-mark.

High Quality Arabic Concatenative Speech Synthesis

This paper describes the implementation of TD-PSOLA tools to improve the quality of the Arabic Text-tospeech (TTS) system. This system based on Diphone concatenation with TD-PSOLA modifier synthesizer. This paper describes techniques to improve the precision of prosodic modifications in the Arabic speech synthesis using the TD-PSOLA (Time Domain Pitch Synchronous Overlap-Add) method. This approach is based on the decomposition of the signal into overlapping frames synchronized with the pitch period. The main objective is to preserve the consistency and accuracy of the pitch marks after prosodic modifications of the speech signal and diphone with vowel integrated database adjustment and optimisation.

A new speech synthesis algorithm for high quality TTS systems: a mixed phase vocoder

AbstractCurrently, time domain pitch synchronous overlap‐add (TD‐PSOLA) is the most popular synthesis algorithm for text‐to‐speech (TTS) systems. The algorithm produces very high quality synthetic speech, particularly when a pitch modification factor is small. However, as the pitch modification factor becomes larger, the quality degradation due to a slight pitch epoch detection error becomes severe. On the other hand, the vocoder framework has very flexible prosody manipulation. It can obtain a uniform voice quality over a wide range of prosody modification. Unfortunately, the synthesized speech quality from the vocoder is far from natural human speech, often showing buzzy quality. To remedy buzzy quality from the vocoder and make more natural synthetic speech, we propose a new speech synthesis algorithm for high quality TTS systems that is based on the homomorphic vocoder framework. The impulse response of vocal tract is obtained by mixing the minimum phase in lower frequency band and original phase in higher frequency band—thus, the name is a mixed phase vocoder. Informal subjective listening tests reveal that the mixed phase vocoder is a good candidate for TTS synthesis with high intelligibility and naturalness. Copyright © 2004 AEI.

A novel prosodic-information synthesizer based on recurrent fuzzy neural network for the Chinese TTS system.

In this paper, a new technique for the Chinese text-to-speech (TTS) system is proposed. Our major effort focuses on the prosodic information generation. New methodologies for constructing fuzzy rules in a prosodic model simulating human's pronouncing rules are developed. The proposed Recurrent Fuzzy Neural Network (RFNN) is a multilayer recurrent neural network (RNN) which integrates a Self-cOnstructing Neural Fuzzy Inference Network (SONFIN) into a recurrent connectionist structure. The RFNN can be functionally divided into two parts. The first part adopts the SONFIN as a prosodic model to explore the relationship between high-level linguistic features and prosodic information based on fuzzy inference rules. As compared to conventional neural networks, the SONFIN can always construct itself with an economic network size in high learning speed. The second part employs a five-layer network to generate all prosodic parameters by directly using the prosodic fuzzy rules inferred from the first part as well as other important features of syllables. The TTS system combined with the proposed method can behave not only sandhi rules but also the other prosodic phenomena existing in the traditional TTS systems. Moreover, the proposed scheme can even find out some new rules about prosodic phrase structure. The performance of the proposed RFNN-based prosodic model is verified by imbedding it into a Chinese TTS system with a Chinese monosyllable database based on the time-domain pitch synchronous overlap add (TD-PSOLA) method. Our experimental results show that the proposed RFNN can generate proper prosodic parameters including pitch means, pitch shapes, maximum energy levels, syllable duration, and pause duration. Some synthetic sounds are online available for demonstration.

A hybrid model for text-to-speech synthesis

This paper describes a hybrid model developed for high-quality, concatenation-based, text-to-speech synthesis. The speech signal is submitted to a pitch-synchronous analysis and decomposed into a harmonic component, with a variable maximum frequency, plus a noise component. The harmonic component is modeled as a sum of sinusoids with frequencies that are multiples of the pitch. The noise component is modeled as a random excitation applied to an LPC filter. In unvoiced segments, the harmonic component is made equal to zero. In the presence of pitch modifications, a new set of harmonic parameters is evaluated by resampling the spectrum envelope at the new harmonic frequencies. For the synthesis of the harmonic component in the presence of duration and/or pitch modifications, a phase correction is introduced into the harmonic parameters. The sinusoidal model of synthesis is used for the harmonic component and the LPC model combined with an overlap and add procedure is used for the noise synthesis. This hybrid model enables independent and continuous control of duration and pitch of the synthesized speech. Comparative evaluation tests made in a text-to-speech environment have shown that the hybrid model assures a better performance than the time-domain pitch synchronous overlap-add (TD-PSOLA) model.

MBR-PSOLA: Text-To-Speech synthesis based on an MBE re-synthesis of the segments database

The use of the Time-Domain Pitch Synchronous OverLap-Add (TD-PSOLA) algorithm in a Text-To-Speech synthesizer is reviewed. Its drawbacks are underlined and three conditions on the speech database are examined. In order to satisfy them, a previously described high quality resynthesis process is developed and enhanced, which makes use of the well-known Multi-Band Excited (MBE) model. An important by-product of this operation is that optimal Pitch Marking turns out to be automatic. A temporal interpolation block is finally added. The resulting Multi-Band Resynthesis Pitch Synchronous OverLap Add (MBR-PSOLA) synthesis algorithm supports spectral interpolation between voiced parts of segments, with virtually no increase in complexity. It provides the basis of a high-quality Text-To-Speech (TTS) synthesizer.