LPC Method Research Articles

Adapted Weighted Linear Prediction with Attenuated Main Excitation for formant frequency estimation in high-pitched singing

This paper aims to show how to improve the accuracy of formant frequency estimation in the singing voice of a lyric soprano. Conventional methods of formant frequency estimation may not accurately capture the formant frequencies of the singing voice, particularly in the highest pitch range of a lyric soprano, where the lowest formants are biased by the pitch harmonics. To address this issue, the study proposes adapting the Weighted Linear Prediction with Attenuated Main Excitation (WLP-AME) method for formant frequency estimation. Specific methods for glottal closure instant estimation were required due to differences in glottal closure patterns between speech and singing. The study evaluates the accuracy of the proposed method by comparing its performance with the LPC method through different pitch series arranged in an ascending musical scale. The results indicated that the adapted WLP-AME method consistently outperformed the LPC method in estimating formant frequencies of vowels sung by a lyric soprano. In addition, by estimating the formant frequencies of a synthetic /i/ vowel sung by a soprano singer at the musical note E5, the study showed that the adapted WLP-AME method provided formant frequency values closer to the correct values than those estimated by the LPC method. In general, these results suggest parameter values of AME function that optimize its performance, which can have applications in fields such as singing and medicine.

Comparison of different Classification techniques for the detection of Speech affected due to Respiratory disorders

Respiratory disorders are one of the common chronic disorders across the world. Any malfunctioning in the respiration process affects speech as speech and respiration go hand in hand. Analysis of speech parameter variation due to respiratory disorders is a prime research area in today’s scenario. In this paper, an efficient approach using a machine learning paradigm was used to detect the speech affected due to respiratory disorders. Various speech parameters were extracted (F1, F2, F3… etc.) using PRAAT software along with MFCC and LPC coefficients. Statistically, significant features were obtained to determine the predominant parameters. The extracted features were applied to different machine learning paradigms then different classification techniques were analyzed and compared to distinguish between normal and affected speech.5-fold,10-fold cross-validation, and hold-out data division protocols were used for evaluating the classification results. To evaluate the performance of the proposed methodology, Accuracy, sensitivity, specificity, and the area under receiver operating characteristics (AUC) were used. The result demonstrates that the speech parameters (F1, F2, F3…etc.) extraction method and the LPC method achieved a significantly higher classification accuracy, sensitivity, and specificity of 100% and AUC of 1, under the holdout method. While MFCC method achieved the highest classification accuracy of 83%, sensitivity of 100%, specificity of 67%, and AUC of 0.83.

Dominant frequency component tracking of noisy time‐varying signals using the linear predictive coding pole processing method

The linear predictive coding pole processing (LPCPP) method proposed in our previous work overcomes the shortcomings of the LPC method, especially its sensitivity to noise and the filter order. The LPCPP method is a parameterised method that involves processing the LPC poles to produce a series of reduced-order filter transfer functions to estimate the dominant frequency components of a signal. This paper analyses the ability of the LPCPP method to track the frequency changes of noisy, time-varying signals in real-time. Linear chirped frequency modulation signals are used in a series of experiments to simulate signals with different rates of frequency change. The results show that the LPCPP method can achieve real-time tracking of the dominant frequency in the signal and outperforms the LPC method under different frequency change rates and different noise levels. Specifically, the valid estimate percentage of LPCPP is up to 41.3% higher than that of LPC which indicates that the LPCPP method significantly improves the validity of frequency estimates.

SPEECH RECOGNITION APPLICATION AS AN ANIMATED OBJECT MOVEMENT CONTROLLER SYSTEM

Technological developments in the world have no boundaries. One of them is Speech Recognition. At first, words spoken by humans cannot be recognized by computers. To be recognizable, the word is processed using a specific method. Linear Predictive Coding Method (LPC) is a method used in this research to extract the characteristics of speech. The result of the LPC method is the LPC coefficient which is the number of LPC orders plus 1. The LPC coefficient is processed using Fast Fourier Transform (FFT) 512 to simplify the process of speech recognition. The results are then trained using Backpropagation Neural Network (BPNN) to recognize the spoken word. Speech recognition on the program is implemented as an animated object motion controller on the computer. The end result of this research is animated objects move in accordance with the spoken word. The optimal BPNN structure in this research is to use traingda training function, number of nodes 3, learning rate 0.05, epoch 1000, performance goal 0,00001. This structure can produce the smallest MSE value that is 0,000009957. So, this structure can recognize new words with 100% accuracy for trained data, 80% for the same respondents with trained data and reach 67.5% for new respondents.

Matlab-Based Formant Estimation

In the study of speech digital signal processing, the resonance peak is the major characteristics in reflecting track resonant. In order to be more conducive to the future development of speech signal processing techniques, three methods of the formant estimation are introduced in the article, namely, Short-time Fourier Transform method, Cepstrum, LPC estimation method. These methods are compared and analyzed by means of MATLAB programming, and it is concluded that LPC method is the best method, which can estimate the formant peaks more accurate.

Feature extraction for vowel recognition using mellin transform

This paper proposed a new feature extraction algorithm for vowel recognition using Mellin transform and MFCC. The scale transform is a particular restriction of the Mellin transform. The key property of the scale transform is the scale invariance. This algorithm consists of 3 main stages which are as follow. First stage contains, Extraction spectral envelope from vowels, by using cepstrum method. In the second stage, Mellin transform is used for mapping same vowels with different pitch and different vocal tract to the same waveforms. In third stage, we used a new MFCC algorithm in order to extract main parameters from the output of second stage. The new MFCC algorithm contains third and fourth order cumulant of log‐mel‐amplitudes. This new MFCC coefficients are robust in presence of additive white noise. Moreover, The LPC method has been used instead of MFCC method in third stage, Experimental result indicates that the new MFCC algorithm produced better result than LPC and original MFCC.

What’s wrong with these formants?

Speech is frequently modeled as an all-pole (autoregressive) linear filter applied to a voice source, commonly called Linear Predictive Coding. This model has been used for many years to provide measurements of the resonance frequencies (formants) characterizing speech sounds. That notwithstanding, it has also been well recognized that the use of this method to measure formants is very often fraught with problems, but just how serious are these problems, and can anything be done to improve upon LPC formant measurements? A few examples will demonstrate that the problems plaguing LPC formant measurements are too often intractable, and frequently yield formant values which are nowhere near the truth. For example, the voice bar will be shown to falsely lower the reported first formant value in many simple cases. It might even be suggested that reliance on this technique has impeded our understanding of exotic speech sounds which do not display a typical formant pattern. Precision formant measurements can be provided in most instances by reassigned spectrograms that have been specially processed to highlight signal components. Examples will be shown and used as benchmarks against which the LPC method can at last be properly evaluated.

Spectral properties of Japanese whispered vowels referred to pitch

Whispered speech can communicate the same linguistic information as ordinary speech in spite of the great difference of their respective acoustic characteristics. In this respect, whispering is an interesting object for studying speech perception and recognition. In this study, we investigate the spectral properties of five whispered Japanese vowels uttered in isolation and having different pitch. Pitch of ordinary whispered vowels was measured in terms of the manner in which the talkers were listening to pure tones while uttering and adjusting its frequency so that the pitch matched the utterance. For other samples, talkers changed the pitch of utterances to match the given pure tones. Acoustic analyses were carried out on formant frequencies, a spectral tilt, and a peak frequency of wide-ranging spectral shape using second-order LPC method called a global peak. Preliminary results show a tendency of upward shift of F1, F2, and global peaks, and flattened spectral tilts on overall vowels with increasing pitch. The intended pitch by a talker is not correspondent to a specific formant frequency. Results of pitch comparison tests among samples and pitch matching test with pure tones will also be discussed. [Work supported by MEXT Japan.]

Digital signals analysis with the LPC method

This paper concerns the issue of signal analysis by the linear predictive method. It is not only about frequency analysis, that is obtaining the LPC spectrum and comparing it with the Fourier one, but also about analysing prediction coefficients themselves. We will also discuss the program made by the authors of this paper, which beside foregoing functionalities, enables vocal tract visualization, modelled on the basis of LPC coefficients.

Modélisation des mouvements articulatoires par la méthode de la LPC multi-impulsionnelle

Are articulatory commands organized in terms of phonemes, syllables or others phonetic units? We attempt to examine this question by modeling articulatory movements with the help of an LPC method where the excitation source is represented by a multi-pulse sequence. Our results deviate from the traditional phonemic-concatenation theory and favor that the elementary gesture in speech production corresponds to the syllabic gesture

A Japanese text‐to‐speech conversion system using pitch‐controlled residual wave excitation

A Japanese text‐to‐speech conversion system was developed that could generate fluent speech by concatenating CV and VC units. A new synthesis method was proposed, where residual waves were used as excitation signals for an LPC synthesis filter in all portions of each unit. LPC filter coefficients were calculated by approximating spectral envelopes extracted by the improved cepstral analysis method, which was less affected by pitch frequency than the conventional LPC method. Therefore, the synthetic speech generated by the proposed method had extremely high quality, even when pitch frequencies of the residual waves were widely changed. Moreover, in order to realize natural rhythms, phoneme duration was determined by new rules. The rules were created by taking into account the various contexts (such as preceding and following phonemes and the position of a phoneme in a phrase), and by statistically analyzing a large speech database. A comprehension test using 1000 phonetic balanced words showed that the proposed system achieved 97.4% of high accuracy rate.

Vocal tract areas from LPC—A comparison of several techniques

The need for good initial estimates of cross‐sectional vocal tract areas [e.g., Schroeter, Larar, and Sondhi, J. Acoust. Soc. Am. Suppl. 1 80, S19 (1986)] has renewed our interest in LPC techniques. Starting with speech synthesized from a vocal tract model with known cross‐sectional areas [Schroeter and Sondhi, J. Acoust. Soc. Am. Suppl. 1 78, S6 (1985)], LPC‐derived areas that closely approximate the areas used to control the synthesizer are sought. In a comparative study, the LPC method used to derive the predictor polynomial (autocorrelation, covariance, and stabilized covariance methods) as well as its particular application with respect to location of pitch epochs is varied; pitch synchronous and asynchronous analyses are performed with different assumptions regarding the glottal termination. Spectral shaping and source components in the inverse filter are removed and formant shifts due to yielding wall losses are considered. Areas are computed using the standard Wakita recursion [Wakita, IEEE Trans. ASSP‐27, 281–285 (1979)] proceeding from the glottis to the lips with the first supraglottal section set to the known value (used for synthesis). Results are presented for steady vowels and for voiced segments produced with a time varying vocal tract.

Speech analysis and synthesis methods developed at ECL in NTT — From LPC to LSP —

Speech analysis and synthesis methods developed at ECL in NTT are described. Starting from the LPC method the PARCOR and LSP schemes based on all pole filter are explained. The principles and physical interpretations of these methods are presented in comparison with each other. The characteristics of the feature parameters in these methods are clarified by means of several experiments. Synthesized speech quality is also shown by objective and subjective experiments. High quality synthesized speech can be achieved by these methods at a low bit rate transmission under 9600 bps.

A Single Chip Digital Signal Processor and Its Application to Real-Time Speech Analysis

A single chip high-performance digital signal processor (HSP) has been developed for speech, telecommunication, and other applications. The HSP uses 3 µm CMOS technology and its architecture features floating point arithmetic and pipeline structure. By adoption of floating point arithmetic, data covering a wide dynamic range (up to 32 bits) can be manipulated. The input clock frequency is 16 MHz, and the instruction cycle time is 250 ns. Efficient signal processing instructions and a large internal memory (program ROM: 512 words; data RAM: 200 words; data ROM: 128 words) make it possible to construct a compact speech analysis circuit by the LPC (PARCOR) method with two HSP's. This paper describes HSP architecture, LSI design, and a speech analysis application.

A single chip digital signal processor and its application to real-time speech analysis

A single chip high-performance digital signal processor (HSP) has been developed for speech, telecommunication, and other applications. The HSP uses 3 μm CMOS technology and its architecture features floating point arithmetic and pipeline structure. By adoption of floating point arithmetic, data covering a wide dynamic range (up to 32 bits) can be manipulated. The input clock frequency is 16 MHz, and the instruction cycle time is 250 ns. Efficient signal processing instructions and a large internal memory (program ROM: 512 words; data RAM: 200 words; data ROM: 128 words) make it possible to construct a compact speech analysis circuit by the LPC (PARCOR) method with two HSP's. This paper describes HSP architecture, LSI design, and a speech analysis application.

Direct sample interpolation (DSI) speech synthesis: An interpolation technique for digital speech data compression and speech synthesis

Direct transcription of the waveform is a potentially simple method to generate speech. However, it has a poor reputation due to the enormous data store required, although many digital encoding techniques have been suggested which reduce the data store significantly. Digital encoding is not invoked but a method called direct sample interpolation (DSI) is described which will compute bridging sections quite simply between, in the first instance, very short vowel sections called here phoneme fragments (PF's). This reduces the data store for vowels. DSI will significantly compress the store for other sounds, notably the fricatives. The method relies on the property in speech that the spectrum changes relatively slowly with time. Perhaps, most importantly, DSI can produce bridging sections of predetermined durations and pitch contouring and can therefore provide facilities for pitch inflexion and stress. Speech generation can be effected using DSI and the hardware is simpler than required for file "straight" LPC method. Speech synthesizers using the method have been built and some details of the data store and the hardware are given.