Blind estimation of speech transmission index and room acoustic parameters based on the extended model of room impulse response

Duangpummet, Suradej; Karnjana, Jessada; Kongprawechnon, Waree; Unoki, Masashi

doi:10.1016/j.apacoust.2021.108372

Cited by 13 publications

(16 citation statements)

References 17 publications

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“…Proposed blind estimation methods for STI and RAPs are based on either analytical or learning-based approaches [4], [19], [20], [21], [22], [23], [24], [29], [30], [31], [32], [33], [34], [35], [36], [37]. Ones following the analytical approach achieve blind estimation by creating an explicit mapping between the observed reverberant signals and the desired parameters.…”

Section: Related Workmentioning

confidence: 99%

“…For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ experiments are expensive and time-consuming, which makes them impractical to apply in public spaces [4]. Hence, objective indices and room acoustic parameters (RAPs), i.e., the physical descriptions of room acoustics [5], [6], [7], have been proposed for use in the subjective evaluations of auditory spaces.…”

Section: Introductionmentioning

confidence: 99%

“…Lopez-Ballester et al proposed the CNN-based algorithm applied in Internet of Things for blind estimation of the reverberation time [24]. Duangpummet et al proposed the temporal-amplitude-envelope (TAE) based CNN for simultaneously estimating the STI and five essential RAPs, which is the first approach to achieve six-parameter blind estimation simultaneously [4]. Although many methods have been proposed, they provide only a single RAP or STI and require a massive amount of training data, which is rarely available in spite of the fact that the environments differs from the training data.…”

Section: Introductionmentioning

confidence: 99%

“…• A deterministic estimation method for blindly estimating STI and five RAPs simultaneously is presented that has estimation accuracy comparable to that of the stateof-the-art method [4]. This paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

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Blind Estimation of Speech Transmission Index and Room Acoustic Parameters by Using Extended Model of Room Impulse Response Derived From Speech Signals

2023

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The speech transmission index (STI) and room acoustic parameters (RAPs) are essential metrics for assessing speech quality and predicting listening difficulty in a sound field. Although STI and important RAPs, such as reverberation time and clarity, can be derived from the room impulse response (RIR), measuring the RIR in regularly occupied spaces is difficult. Hence, simultaneous blind estimation of STI and RAPs is an imperative challenge issue that must be addressed. However, most existing methods provide only a single parameter and require a massive dataset for model training. A deterministic method is presented for blindly estimating STI and five RAPs using a stochastic RIR model that approximates an unknown RIR. An algorithm is formulated that uses the temporal power envelope of a reverberant speech signal to determine the optimal parameters of the RIR model. A mathematical model of reverberation and dereverabation process was proposed based on the temporal power envelope of the signals. This model maps the parameters of the RIR model to the observed reverberant signal. The estimated RIR can then be synthesized using the optimal parameters to estimate the STI and RAPs. A simulation was conducted to evaluate the simultaneous estimation of STI and five essential RAPs from observed reverberant speech signals, in comparison to the best existing previous work. The root-mean-square error (RMSE) and Pearson correlation coefficient between the estimated and measured values were used as evaluation metrics. In terms of STI, the proposed method achieves the accuracy with an RMSE of 0.037. With regard to the reverberation time and other RAPs, the accuracy remains consistent with the previous works. The results show that the proposed method can effectively estimate STI and RAPs simultaneously without any training.INDEX TERMS Room impulse response, modulation transfer function, reverberation, speech transmission index, room acoustic parameters.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Blind Estimation of Speech Transmission Index and Room Acoustic Parameters by Using Extended Model of Room Impulse Response Derived From Speech Signals

2023

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“…Further, it is found that quality [9], [10] and intelligibility [11], [12] affect ASR performance. Therefore, a drop in the performance of the general purpose automatic speech recognition (gp-asr) due to incorrect estimation of RIR is due to deterioration of speech quality [13], [14], [15] and speech intelligibility [16], [17]. It is therefore, plausible to assume that enhancing the performance of gp-asr will result in a better user-experience, which depends on accurate estimation of RIR.…”

Section: Introductionmentioning

confidence: 99%

Learning Optimal Microphone Location for Enhanced ASR Performance using Limited Data

Biswas¹,

Nathwani²,

Kopparapu³

2022

Preprint

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<p>The placement of a microphone at its correct position is crucial for automatic speech recognition (ASR) in a single-microphone distant speech recognition setup. Understandably, it is a practical problem that the performance degrades due to accidental displacement of the microphone from the correct position through routine use and maintenance. In this paper, an approach is formulated which makes it possible to determine the optimal position of the microphone, given the geometry of the room, the location of the speaker, and the inexact location of the microphone. The metric used to measure the correctness of the microphone position is the average character error rate (cer) of the ASR system at that position. The proposed approach uses limited available data which is usually the case when dealing with microphone position error analysis while considering room acoustics. A neural network classifier is trained using limited data to associate the location of the microphone with the corresponding class of cer values. The obtained prediction is used to trace the microphone back to the optimal position.</p>

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Development of a speech separation system using frequency domain blind source separation technique

Sharma,

Kumar,

Meena

2023

Multimed Tools Appl

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Blind estimation of speech transmission index and room acoustic parameters based on the extended model of room impulse response

Cited by 13 publications

References 17 publications

Blind Estimation of Speech Transmission Index and Room Acoustic Parameters by Using Extended Model of Room Impulse Response Derived From Speech Signals

Blind Estimation of Speech Transmission Index and Room Acoustic Parameters by Using Extended Model of Room Impulse Response Derived From Speech Signals

Learning Optimal Microphone Location for Enhanced ASR Performance using Limited Data

Development of a speech separation system using frequency domain blind source separation technique

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