Highly-Reverberant Real Environment database: HRRE
نویسندگان
چکیده
Speech recognition in highly-reverberant real environments remains a major challenge. An evaluation dataset for this task is needed. This report describes the generation of the Highly-Reverberant Real Environment database (HRRE). This database contains 13.4 hours of data recorded in real reverberant environments and consists of 20 different testing conditions which consider a wide range of reverberation times and speaker-to-microphone distances. These evaluation sets were generated by re-recording the clean test set of the Aurora-4 database which corresponds to five loudspeaker-microphone distances in four reverberant conditions. Database Recording To generate the data for the test set, we re-recorded the original clean test data from the Aurora-4 database (i.e. 330 utterances recorded with the Sennheiser microphone) in a reverberation chamber considering different speakermicrophone distances and reverberation times (RTs) and following the procedures specified by the ISO 354:2003 Standard [1]. The reverberation chamber has an internal surface area of 100 m, a volume of 63 m and an RTmid equal to three seconds. Four reverberant conditions were generated by adding sound-absorbing materials in the reflecting surfaces of the chamber. The
منابع مشابه
Exploring the robustness of features and enhancement on speech recognition systems in highly-reverberant real environments
This paper evaluates the robustness of a DNN-HMM-based speech recognition system in highly-reverberant real environments using the HRRE database. The performance of locally-normalized filter bank (LNFB) and Mel filter bank (MelFB) features in combination with Non-negative Matrix Factorization (NMF), Suppression of Slowly-varying components and the Falling edge (SSF) and Weighted Prediction Erro...
متن کاملOn the Use of Artificial Reverberation for Asr in Highly Reverberant Environments
In this paper, we discuss the use of artificial room reverberation methods to increase the performance of automatic speech recognition (ASR) systems in highly reverberant enclosures. Our approach consists in training acoustic models on artificially reverberated speech material. In order to obtain the desired reverberated speech training database, we propose to use a reverberating filter whose i...
متن کاملPerformance estimation of speech recognition based on acoustic parameters under reverberation environments with CENSREC-4
The Corpus and Environment for Noisy Speech RECognition 4 (CENSREC-4) evaluation framework has been distributed for evaluating distant-talking speech under various reverberation environments. The CENSREC-4 includes both real and simulated reverberant speech with convoluting impulse responses in the same environment. In addition, it consists of many room impulse responses to simulate various env...
متن کاملAn MTF-based blind restoration of temporal power envelopes as a front-end processor for automatic speech recognition systems in reverberant environments
To reduce speech degradation in reverberant environments, we previously proposed a modulation transfer function (MTF) based method of speech restoration. The room impulse response (RIR) in this restoration does not need to be measured at any time since we modeled the power envelope of the RIRs as an exponential decay function. Speech is assumed to be temporal modulated with white noise carrier ...
متن کاملSpeech Recognition by Denoising and Dereverberation Based on Spectral Subtraction in a Real Noisy Reverberant Environment
A blind dereverberation method based on spectral subtraction using a multi-channel least mean squares algorithm was previously proposed. The results of a large vocabulary continuous speech recognition task showed that this method achieved significant improvements over the conventional method based on cepstral mean normalization and beamforming in a simulated reverberant environment without addi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- CoRR
دوره abs/1801.09651 شماره
صفحات -
تاریخ انتشار 2018