Browsing by Author "Gowda, Dhananjaya"

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  • Formant tracking using quasi-closed phase forward-backward linear prediction analysis and deep neural networks
    (2021) Gowda, Dhananjaya; Bollepalli, Bajibabu; Kadiri, Sudarsana; Alku, Paavo
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Formant tracking is investigated in this study by using trackers based on dynamic programming (DP) and deep neural nets (DNNs). Using the DP approach, six formant estimation methods were first compared. The six methods include linear prediction (LP) algorithms, weighted LP algorithms and the recently developed quasi-closed phase forward-backward (QCP-FB) method. QCP-FB gave the best performance in the comparison. Therefore, a novel formant tracking approach, which combines benefits of deep learning and signal processing based on QCP-FB, was proposed. In this approach, the formants predicted by a DNN-based tracker from a speech frame are refined using the peaks of the all-pole spectrum computed by QCP-FB from the same frame. Results show that the proposed DNN-based tracker performed better both in detection rate and estimation error for the lowest three formants compared to reference formant trackers. Compared to the popular Wavesurfer, for example, the proposed tracker gave a reduction of 29%, 48%, and 35% in the estimation error for the lowest three formants, respectively.
  • Quasi-closed phase forward-backward linear prediction analysis of speech for accurate formant detection and estimation
    (2017-09-25) Gowda, Dhananjaya; Airaksinen, Manu; Alku, Paavo
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    Recently, a quasi-closed phase (QCP) analysis of speech signals for accurate glottal inverse filtering was proposed. However, the QCP analysis which belongs to the family of temporally weighted linear prediction (WLP) methods uses the conventional forward type of sample prediction. This may not be the best choice especially in computing WLP models with a hard-limiting weighting function. A sample selective minimization of the prediction error in WLP reduces the effective number of samples available within a given window frame. To counter this problem, a modified quasi-closed phase forward-backward (QCP-FB) analysis is proposed, wherein each sample is predicted based on its past as well as future samples thereby utilizing the available number of samples more effectively. Formant detection and estimation experiments on synthetic vowels generated using a physical modeling approach as well as natural speech utterances show that the proposed QCP-FB method yields statistically significant improvements over the conventional linear prediction and QCP methods.
  • Refining a Deep Learning-based Formant Tracker using Linear Prediction Methods
    (2023-06) Alku, Paavo; Kadiri, Sudarsana; Gowda, Dhananjaya
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    In this study, formant tracking is investigated by refining the formants tracked by an existing data-driven tracker, DeepFormants, using the formants estimated in a model-driven manner by linear prediction (LP) -based methods. As LP-based formant estimation methods, conventional covariance analysis (LP-COV) and the recently proposed quasi-closed phase forward-backward (QCP-FB) analysis are used. In the proposed refinement approach, the contours of the three lowest for- mants are first predicted by the data-driven DeepFormants tracker, and the predicted formants are replaced frame-wise with local spectral peaks shown by the model-driven LP-based methods. The refinement procedure can be plugged into the DeepFormants tracker with no need for any new data learning. Two refined DeepFormants trackers were compared with the original DeepFormants and with five known traditional trackers using the popular vocal tract resonance (VTR) corpus. The results indicated that the data-driven DeepFormants trackers outperformed the conventional trackers and that the best performance was obtained by refining the formants predicted by DeepFormants using QCP-FB analysis. In addition, by tracking formants using VTR speech that was corrupted by additive noise, the study showed that the refined DeepFormants trackers were more resilient to noise than the reference trackers. In general, these results suggest that LP-based model-driven approaches, which have traditionally been used in formant estimation, can be combined with a modern data-driven tracker easily with no further training to improve the tracker’s performance.
  • Segment phoneme classification from speech under noisy conditions: Using amplitude-frequency modulation based two-dimensional auto-regressive features with deep neural networks
    (2016-08-24) Rangslang, Rijuban
     Sähkötekniikan korkeakoulu | Master's thesis
    This thesis investigates at the acoustic-phonetic level the noise robustness of features derived using the AM-FM analysis of speech signals. The analysis on the noise robustness of these features is done using various neural network models and is based on the segment classification of phonemes. This analysis is also extended and the robustness of the AM-FM based features is compared under similar noise conditions with the traditional features such as the Mel-frequency cepstral coefficients(MFCC). We begin with an important aspect of segment phoneme classification experiments which is the study of architectural and training strategies of the various neural network models used. The results of these experiments showed that there is a difference in the training pattern adopted by the various neural network models. Before over-fitting, models that undergo pre-training are seen to train for many epochs more than their opposite models that do not undergo pre-training. Taking this difference in training pattern into perspective and based on phoneme classification rate the Gaussian restricted Boltzmann machine and the single layer perceptron are selected as the best performing model of the two groups, respectively. Using the two best performing models for classification, segment phoneme classification experiments under different noise conditions are performed for both the AM-FM based and traditional features. The experiments showed that AM-FM based frequency domain linear prediction features with or without feature compensation are more robust in the classification of 61 phonemes under white noise and 0 $dB$ signal-to-noise ratio(SNR) conditions compared to the traditional features. However, when the phonemes are folded to 39 phonemes, the results are ambiguous under all noise conditions and there is no unanimous conclusion as to which feature is most robust.
  • Time-varying autoregressions for speaker verification in reverberant conditions
    (2017-08) Vestman, Ville; Gowda, Dhananjaya; Sahidullah, Md; Alku, Paavo; Kinnunen, Tomi
     A4 Artikkeli konferenssijulkaisussa
    In poor room acoustics conditions, speech signals received by a microphone might become corrupted by the signals’ delayed versions that are reflected from the room surfaces (e.g. wall, floor). This phenomenon, reverberation, drops the accuracy of automatic speaker verification systems by causing mismatch between the training and testing. Since reverberation causes temporal smearing to the signal, one way to tackle its effects is to study robust feature extraction, particularly based on long-time temporal feature extraction. This approach has been adopted previously in the form of 2-dimensional autoregressive (2DAR) feature extraction scheme by using frequency domain linear prediction (FDLP). In 2DAR, FDLP processing is followed by time domain linear prediction (TDLP). In the current study, we propose modifying the latter part of the 2DAR feature extraction scheme by replacing TDLP with time-varying linear prediction (TVLP) to add an extra layer of temporal processing. Our speaker verification experiments using the proposed features with the text-dependent RedDots corpus show small but consistent improvements in clean and reverberant conditions (up to 6.5%) over the 2DAR features and large improvements over the MFCC features in reverberant conditions (up to 46.5%).
  • Time-varying quasi-closed-phase analysis for accurate formant tracking in speech signals
    (2020-06-04) Gowda, Dhananjaya; Kadiri, Sudarsana; Story, Brad; Alku, Paavo
     A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
    In this paper, we propose a new method for the accurate estimation and tracking of formants in speech signals using time-varying quasi-closed-phase (TVQCP) analysis. Con-ventional formant tracking methods typically adopt a two-stage estimate-and-track strategy wherein an initial set of formant candidates are estimated using short-time analysis (e.g., 10–50 ms), followed by a tracking stage based on dynamic programming or a linear state-space model. One of the main disadvantages of these approaches is that the tracking stage, however good it may be, cannot improve upon the formant estimation accuracy of the first stage. The proposed TVQCP method provides a single-stage formant tracking that combines the estimation and tracking stages into one. TVQCP analysis combines three approaches to improve formant estimation and tracking: (1) it uses temporally weighted quasi-closed-phase analysis to derive closed-phase es-timates of the vocal tract with reduced interference from the excitation source, (2) it increases the residual sparsity by using the L1 optimization and (3) it uses time-varying linear prediction analysis over long time windows (e.g., 100–200 ms) to impose a continuity constraint on the vocal tract model and hence on the formant trajectories. Formant tracking experiments with a wide variety of synthetic and natural speech signals show that the proposed TVQCP method performs better than conventional and popular formant tracking tools, such as Wavesurfer and Praat (based on dynamic programming), the KARMA algorithm (based on Kalman filtering), and DeepFormants (based on deep neural networks trained in a supervised manner).