95,920 research outputs found
Speech Recognition Using HMM/ANN Hybrid Model
By the analysis on the principle of speech recognition system, a speech recognition system was designed by using LPC2148 as the hardware platform and MATLAB 2012 as the software platform. Speech recognition is an important component of biological identification which is an integrated technology of acoustics, signal processing and artificial intelligence. Recognition systems based on hidden Markov models are effective under particular circumstances, but do suffer from some major limitations that limit applicability of ASR technology in real-world environments. Attempts were made to overcome these limitations with the adoption of artificial neural networks as an alternative paradigm for ASR, but ANNs were unsuccessful in dealing with long time sequences of speech signals. So taking the limitations and advantages of both the systems it was proposed to combine HMM and ANN within a single, hybrid architecture. The goal in hybrid systems for ASR is to take advantage from the properties of both HMM and ANNs, improving flexibility and ASR performance For Speech recognition features from speech sample are extracted & mapping is done using Artificial Neural Networks. Multilayer pattern mapping neural network, which works on the principle of back propagation algorithm is proposed. Finally Speaker Recognition is done using Hidden Markov Model (HMM). The specialty of this model is the flexible and expandable hidden layer for recognition.
DOI: 10.17762/ijritcc2321-8169.150613
DESIGN & IMPLEMENTATION OF A REAL-TIME, SPEAKER-INDEPENDENT, CONTINUOUS SPEECH RECOGNITION SYSTEM WITH VLIW DIGITAL SIGNAL PROCESSOR ARCHITECTURE
This thesis explores the feasibility of mapping a real-time, continuous speech recognition system onto a multi-core Digital Signal Processor architecture. While a pure hardware solution is capable of implementing the entire recognition process in real-time, the design process can be lengthy and inflexible to changes. However, a low-end embedded processor such as ARM7 is insufficient to execute in real-time. As a result, a more flexible and powerful DSP solution with Texas Instruments¡¦ C6713 multi-core DSP is used to exploit the instruction level parallelism within the speech recognition process. By exploiting the parallelism using 7 optimization techniques, the performance of the recognition process can be real-time on a 300 MHz DSP for a 1000 word vocabulary. At its core, continuous speech recognition is essentially a matching problem. The recognition process can be divided into four major phases: Feature Extraction, Acoustic Modeling, Phone Modeling and Word Modeling. Each phase is analyzed in detail to identify performance issues. In short, the major issues are its massive computations and large memory bandwidth. After applying various optimizations, the overall computational performance has improved from about 15 times slower than real-time to 1.6 times faster than real-time with the hardware. Through utilization of Direct Memory Access and larger cache memory, the memory bandwidth problem can be solved. The conclusion is that a multi-core DSP running at 300 MHz would be sufficient to implement a 1000 word Command & Control type application using the optimization techniques described in this thesis
GEMINI: A Generic Multi-Modal Natural Interface Framework for Videogames
In recent years videogame companies have recognized the role of player
engagement as a major factor in user experience and enjoyment. This encouraged
a greater investment in new types of game controllers such as the WiiMote, Rock
Band instruments and the Kinect. However, the native software of these
controllers was not originally designed to be used in other game applications.
This work addresses this issue by building a middleware framework, which maps
body poses or voice commands to actions in any game. This not only warrants a
more natural and customized user-experience but it also defines an
interoperable virtual controller. In this version of the framework, body poses
and voice commands are respectively recognized through the Kinect's built-in
cameras and microphones. The acquired data is then translated into the native
interaction scheme in real time using a lightweight method based on spatial
restrictions. The system is also prepared to use Nintendo's Wiimote as an
auxiliary and unobtrusive gamepad for physically or verbally impractical
commands. System validation was performed by analyzing the performance of
certain tasks and examining user reports. Both confirmed this approach as a
practical and alluring alternative to the game's native interaction scheme. In
sum, this framework provides a game-controlling tool that is totally
customizable and very flexible, thus expanding the market of game consumers.Comment: WorldCIST'13 Internacional Conferenc
Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition
Long short-term memory (LSTM) is normally used in recurrent neural network
(RNN) as basic recurrent unit. However,conventional LSTM assumes that the state
at current time step depends on previous time step. This assumption constraints
the time dependency modeling capability. In this study, we propose a new
variation of LSTM, advanced LSTM (A-LSTM), for better temporal context
modeling. We employ A-LSTM in weighted pooling RNN for emotion recognition. The
A-LSTM outperforms the conventional LSTM by 5.5% relatively. The A-LSTM based
weighted pooling RNN can also complement the state-of-the-art emotion
classification framework. This shows the advantage of A-LSTM
Improving speech recognition by revising gated recurrent units
Speech recognition is largely taking advantage of deep learning, showing that
substantial benefits can be obtained by modern Recurrent Neural Networks
(RNNs). The most popular RNNs are Long Short-Term Memory (LSTMs), which
typically reach state-of-the-art performance in many tasks thanks to their
ability to learn long-term dependencies and robustness to vanishing gradients.
Nevertheless, LSTMs have a rather complex design with three multiplicative
gates, that might impair their efficient implementation. An attempt to simplify
LSTMs has recently led to Gated Recurrent Units (GRUs), which are based on just
two multiplicative gates.
This paper builds on these efforts by further revising GRUs and proposing a
simplified architecture potentially more suitable for speech recognition. The
contribution of this work is two-fold. First, we suggest to remove the reset
gate in the GRU design, resulting in a more efficient single-gate architecture.
Second, we propose to replace tanh with ReLU activations in the state update
equations. Results show that, in our implementation, the revised architecture
reduces the per-epoch training time with more than 30% and consistently
improves recognition performance across different tasks, input features, and
noisy conditions when compared to a standard GRU
- …