Single- and Two-channel Noise Reduction for Robust Speech Recognition

نویسندگان

  • Stefanie Aalburg
  • Christophe Beaugeant
  • Sorel Stan
  • Tim Fingscheidt
  • Radu Balan
  • Justinian Rosca
چکیده

Hands-free operation of a mobile phone in car raises major challenges for acoustic enhancement algorithms and speech recognition engines. This is due to a degradation of the speech signal caused by reverberation effects and engine noise. In a typical mobile phone/carkit configuration only the car-kit microphone is used. A legitimate question is whether it is possible to improve the useful signal using the input from the second microphone, namely the microphone of the mobile terminal. In this paper we show that a speech enhancement algorithm specifically developed for two input channels significantly increases the word recognition rates in comparison with singlechannel noise reduction techniques.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Improving the performance of MFCC for Persian robust speech recognition

The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to t...

متن کامل

A Novel Frequency Domain Linearly Constrained Minimum Variance Filter for Speech Enhancement

A reliable speech enhancement method is important for speech applications as a pre-processing step to improve their overall performance. In this paper, we propose a novel frequency domain method for single channel speech enhancement. Conventional frequency domain methods usually neglect the correlation between neighboring time-frequency components of the signals. In the proposed method, we take...

متن کامل

روشی جدید در بازشناسی مقاوم گفتار مبتنی بر دادگان مفقود با استفاده از شبکه عصبی دوسویه

Performance of speech recognition systems is greatly reduced when speech corrupted by noise. One common method for robust speech recognition systems is missing feature methods. In this way, the components in time - frequency representation of signal (Spectrogram) that present low signal to noise ratio (SNR), are tagged as missing and deleted then replaced by remained components and statistical ...

متن کامل

A Deep Neural Network Approach for Missing-Data Mask Estimation on Dual-Microphone Smartphones: Application to Noise-Robust Speech Recognition

The inclusion of two or more microphones in smartphones is becoming quite common. These were originally intended to perform noise reduction and few benefit is still being taken from this feature for noise-robust automatic speech recognition (ASR). In this paper we propose a novel system to estimate missing-data masks for robust ASR on dual-microphone smartphones. This novel system is based on d...

متن کامل

A New Method for Speech Enhancement Based on Incoherent Model Learning in Wavelet Transform Domain

Quality of speech signal significantly reduces in the presence of environmental noise signals and leads to the imperfect performance of hearing aid devices, automatic speech recognition systems, and mobile phones. In this paper, the single channel speech enhancement of the corrupted signals by the additive noise signals is considered. A dictionary-based algorithm is proposed to train the speech...

متن کامل

Single- and Two-channel Noise Reduction for Robust Speech Recognition in Car

Hands-free operation of a mobile phone in car raises major challenges for acoustic enhancement algorithms and speech recognition engines. This is due to a degradation of the speech signal caused by reverberation effects and engine noise. In a typical mobile phone/carkit configuration only the car-kit microphone is used. A legitimate question is whether it is possible to improve the useful signa...

متن کامل

ذخیره 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 نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2005