21-05-2012, 05:32 PM
HARDWARE–SOFTWARE CODE SIGN OF AUTOMATIC SPEECH RECOGNITION SYSTEM FOR EMBEDDED REAL-TIME APPLICATIONS
ABSTRACT
We present a hardware-software coprocessing speech recognizer for real-time embedded applications. The system consists of a standard microprocessor and a hardware accelerator for Gaussian mixture model (GMM) emission probability calculation implemented on a field-programmable gate array. The GMM accelerator is optimized for timing performance by exploiting data parallelism.
In order to avoid large memory requirement, the accelerator adopts a double buffering scheme for accessing the acoustic parameters with no assumption made on the access pattern of these parameters.
Experiments on widely used benchmark data show that the real-time factor of the proposed system is 0.62, which is about three times faster than the pure software-based baseline system, while the word accuracy rate is preserved at 93.33%.
As a part of the recognizer, a new adaptive beam-pruning algorithm is also proposed and implemented, which further reduces the average real-time factor to 0.54 with the word accuracy rate of 93.16%. The proposed speech recognizer is suitable for integration in various types of voice (speech)-controlled applications.
ABSTRACT
We present a hardware-software coprocessing speech recognizer for real-time embedded applications. The system consists of a standard microprocessor and a hardware accelerator for Gaussian mixture model (GMM) emission probability calculation implemented on a field-programmable gate array. The GMM accelerator is optimized for timing performance by exploiting data parallelism.
In order to avoid large memory requirement, the accelerator adopts a double buffering scheme for accessing the acoustic parameters with no assumption made on the access pattern of these parameters.
Experiments on widely used benchmark data show that the real-time factor of the proposed system is 0.62, which is about three times faster than the pure software-based baseline system, while the word accuracy rate is preserved at 93.33%.
As a part of the recognizer, a new adaptive beam-pruning algorithm is also proposed and implemented, which further reduces the average real-time factor to 0.54 with the word accuracy rate of 93.16%. The proposed speech recognizer is suitable for integration in various types of voice (speech)-controlled applications.