Blind estimation of room acoustic parameters from speech signals based on extended model of room impulse response

TL;DR

This paper introduces a deterministic, training-free method for blind estimation of room acoustic parameters and speech transmission index from reverberant speech signals, using a stochastic RIR model.

Contribution

It proposes a novel approach that estimates room acoustic parameters directly from speech signals without prior RIR measurements or training.

Findings

Effective estimation of STI and RAPs demonstrated in simulations.

Outperforms previous methods in accuracy based on RMS error comparison.

No training required, simplifying practical application.

Abstract

The speech transmission index (STI) and room acoustic parameters (RAPs), which are derived from a room impulse response (RIR), such as reverberation time and early decay time, are essential to assess speech transmission and to predict the listening difficulty in a sound field. Since it is difficult to measure RIR in daily occupied spaces, simultaneous blind estimation of STI and RAPs must be resolved as it is an imperative and challenging issue. This paper proposes a deterministic method for blindly estimating STI and five RAPs on the basis of an RIR stochastic model that approximates an unknown RIR. The proposed method formulates a temporal power envelope of a reverberant speech signal to obtain the optimal parameters for the RIR model. Simulations were conducted to evaluate STI and RAPs from observed reverberant speech signals. The root-mean-square errors between the estimated and ground-truth results were used to comparatively evaluate the proposed method with the previous method. The results showed that the proposed method can estimate STI and RAPs effectively without any training.