Neural network, a method of learning of a neural network and phoneme
recognition apparatus utilizing a neural network
    1.
    发明授权
    Neural network, a method of learning of a neural network and phoneme recognition apparatus utilizing a neural network 失效
    神经网络,神经网络的学习方法和利用神经网络的音素识别装置

    公开(公告)号:US06026358A

    公开(公告)日:2000-02-15

    申请号:US576585

    申请日:1995-12-21

    申请人: Hideto Tomabechi

    发明人: Hideto Tomabechi

    CPC分类号: G06N3/049 G10L15/16

    摘要: A neuron device network is provided with a speech input layer, a context layer, a hidden layer, a speech output layer and a hypothesis layer. A phoneme to be learned is spectral-analyzed by an FFT unit and a vector row at a time point t is input to a speech input layer. Also, a vector state of the hidden layer at a time t-1 is input to the context layer, the vector row at a time t+1 is input to the speech output layer as an instructor signal, and a code row for hypothesizing the phoneme, or the code row, is input to the hypothesis layer. The time series relation of the vector rows and the phoneme are hypothetically learned. Alternatively, a spectrum, a cepstrum or a speech vector row based on outputs from the hidden layer of an auto-associative neural network is input to the speech input layer, and the code row is output from the hypothesis layer, taking into account the time series relation. The speech is recognized when a CPU reads the stored output values of the hidden layer and the connection weights of the hidden layer and the hypothesis layer from a memory of the neuron device network and calculates output values of the respective neuron devices of the hypothesis layer based on the output values and the connection weights. The corresponding phoneme is determined by collating the output values of the respective neuron devices of the hypothesis layer with the code rows in an instructor signal table.

    摘要翻译: 神经元设备网络设置有语音输入层,上下文层,隐藏层,语音输出层和假设层。 要学习的音素被FFT单元频谱分析,并且在时间点t的矢量行被输入到语音输入层。 此外,时刻t-1的隐藏层的矢量状态被输入到上下文层,时刻t + 1的矢量行作为指示信号被输入到语音输出层,并且代码行用于假设 音素或代码行被输入到假设层。 假设学习矢量行和音素的时间序列关系。 或者,基于来自自动关联神经网络的隐藏层的输出的频谱,倒谱或语音向量行被输入到语音输入层,并且代码行从假设层输出,考虑时间 系列关系。 当CPU从神经元设备网络的存储器读取隐藏层的存储的输出值和隐藏层和假设层的连接权重时,识别语音,并基于假设层的各个神经元设备的输出值计算 关于输出值和连接权重。 通过将假设层的各个神经元装置的输出值与教师信号表中的代码行进行对照来确定相应的音素。