Patent Publication Number: US-10762427-B2

Title: Connectionist temporal classification using segmented labeled sequence data

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of and priority to U.S. Provisional Patent Application No. 62/465,738 filed Mar. 1, 2017 and entitled “CONNECTIONIST TEMPORAL CLASSIFICATION (CTC) WITH NO LATENCY USING SEGMENTED LABELED SEQUENCE DATA” which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     The present application relates generally to information classification, and more particularly, for example, to systems and methods for training a neural network using segmented labeled data for use in speech recognition systems. 
     BACKGROUND 
     Neural networks are commonly used in information classification systems, such as speech recognition systems for phoneme recognition. In one approach, an Artificial Neural Network (ANN) classifier is combined with a Hidden Markov Model (HMM) to transform network classifications into labeled sequences. The HMM is typically used to model the long range sequential structure of the data while the ANN is used to provide localized classifications. The use of an HMM model, however, requires unnecessary assumptions about the data. A Recurrent Neural Network (RNN) may also be combined with HMMs to label input sequences, but traditional approaches fail to exploit the full potential of RNN for modeling sequential data. 
     Further, many approaches are often highly complex and may not be practical for use in applications with memory, power and processing limitations, such as mobile telephones and other low power devices. Efforts to reduce complexity often come at the cost of less flexibility, memory inefficiencies, and other undesirable performance measures. In view of the foregoing, there is a need in the art for solutions to optimize information classification systems for training neural networks that are both fast and resource efficient. 
     SUMMARY 
     The present disclosure provides systems and methods that address a need in the art for improved classification. In various embodiments, a computer-implemented method for training a neural network for phoneme recognition comprises receiving, at a computing device, a stream of segmented, labeled training data having a sequence of frames, generating neural network outputs for the sequence of frames in a forward pass through the training data and in accordance with weights and biases and updating the weights and biases through a backward pass through the training data. 
     In various embodiments, the backward pass comprises obtaining Region of Target (ROT) information from the stream of segmented, labeled training data, generating a forward-backward masking based on the ROT information, the forward-backward masking placing at least one restriction on a neural network output path, computing modified forward and backward variables based on the neural network outputs and the forward-backward masking, and updating the weights and biases of the neural network. The weights and biases may be updated by one or more of identifying target and shared regions in the stream of segmented, labeled training data, computing a soft target using the forward-backward masking and the neural network outputs (e.g., computing the soft target only for identified target and shared regions), and computing a signal error based on the neural network outputs and the soft target. 
     The weights and biases of the neural network may further be updated by adaptively learning to improve a convergence rate of the neural network, which may include implementing a first algorithm based on momentum for the weights and biases of an output layer of the neural network and implementing a second algorithm based on a Root Mean Square (RMS) of a signal error for other weights and biases. 
     In various embodiments, a classification training system comprises a neural network for use in a classification of input data, a training dataset providing segmented labeled training data, and a classification training module operable to train the neural network using the segmented labeled training data. The classification training module may comprise a forward pass processing module operable to train the neural network by generating neural network outputs for the training data using weights and bias for the neural network, and a backward pass processing module operable to train the neural network by updating the weights and biases in a backward pass. 
     In one or more embodiments, the backward pass module is operable to obtain Region of Target (ROT) information from the segmented, labeled training data, generate a forward-backward masking based on the ROT information, the forward-backward masking placing at least one restriction on a neural network output path, compute modified forward and backward variables based on the neural network outputs and the forward-backward masking, and update the weights and biases of the neural network. The weights and biases may be computed through one or more of computing a soft target using the forward-backward masking and the neural network outputs, computing a signal error based on the neural network outputs and the soft target, identifying target and shared regions in the segmented, labeled training data and computing the soft target only for identified target and shared regions. 
     In various embodiments, the classification training system is further operable to update the weights and biases of the neural network by adaptively learning to improve a convergence rate of the neural network, which may include a first algorithm based on momentum for the weights and biases of an output layer of the neural network, and a second algorithm based on a Root Mean Square (RMS) of a signal error for other weights and biases. 
     In various embodiments, the classification training system may comprise a phoneme recognition system, which operates in accordance with the present disclosure without latency. 
     The scope of the disclosure is defined by the claims, which are incorporated into this section by reference. A more complete understanding of embodiments of the present disclosure will be afforded to those skilled in the art, as well as a realization of additional advantages thereof, by a consideration of the following detailed description of one or more embodiments. Reference will be made to the appended sheets of drawings that will first be described briefly. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       Aspects of the disclosure and their advantages can be better understood with reference to the following drawings and the detailed description that follows. The included drawings are for illustrative purposes and serve only to provide examples of possible systems and methods for the disclosed methods and systems. These drawings in no way limit any changes in form and detail that may be made to that which is disclosed by one skilled in the art without departing from the spirit and scope of this disclosure. 
         FIG. 1  illustrates an exemplary classification system for performing a forward pass on training data to train an artificial neural network, in accordance with an embodiment. 
         FIG. 2  illustrates an exemplary classification system for performing a backward pass on training data to train an artificial neural network, in accordance with an embodiment. 
         FIG. 3  illustrates exemplary Region of Target (ROT) labeling with an exemplary sequence of input data, in accordance with an embodiment. 
         FIG. 4  illustrates an exemplary forward-backward algorithm applied to a labeled sequence of training data, in accordance with an embodiment. 
         FIG. 5  is a flow chart illustrating an exemplary operation of a classification training system, in accordance with an embodiment. 
         FIG. 6  is a block diagram illustrated a classification training system, in accordance with an embodiment. 
         FIG. 7  is a block diagram illustrating a classification system, in accordance with an embodiment. 
     
    
    
     DETAILED DESCRIPTION 
     In accordance with various embodiments of the present disclosure, systems and methods for training a recurrent neural network are disclosed. In one embodiment, a novel Connectionist Temporal Classification (CTC) network is proposed that uses pre-segmented labeled training data. The systems and methods disclosed herein are more effective and computationally efficient than conventional CTC networks, the trained system can operate without latency, and the training methods produce improved (i.e., fast) convergence for CTC networks. 
     Conventional Recurrent Neural Networks (RNNs) can use internal memory to process arbitrary sequences of inputs. This makes RNNs suitable for tasks such as speech recognition since RNNs are able to model both the acoustic and temporal pattern of the input sequences. One approach for temporal classification with RNNs uses a Connectionist Temporal Classification (CTC) network, such as described in Graves, A., Fernandez, S., Gomez, F., Schmidhuber, J., “Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural nets,” Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh-Pennsylvania (U.S.A.) (2006) (hereinafter, “Graves, et al.”), which is incorporated herein by reference in its entirety. One idea behind CTC is that instead of generating a label as output from the neural network, a probability distribution is generated at every time step. This probability distribution may then be decoded into a maximum likelihood label. The network is then trained by creating an objective function that coerces the maximum likelihood decoding for a given sequence to correspond to a desired label. 
     Unlike the approach described above using RNN combined with HMM, CTC network models all aspects of the sequence with a single RNN, and does not require the addition of an HMM to model the temporal pattern of the input sequence. The RNN may be trained directly for temporal classification tasks, which can be achieved by allowing the network to make label predictions at any point in the input sequence provided the overall sequence of labels is correct. Because CTC directly estimates the probabilities of the complete label sequences, external post-processing is not required to use the network as a temporal classifier. 
     Conventional systems, such as the system described in Graves et al., may include frame-wise and CTC networks classifying a speech signal. The system tracks probabilities of observing phonemes at particular times. The CTC network predicts the sequence of phonemes (typically as a series of spikes, separated by ‘blanks’, or null predictions), while the frame-wise network attempts to align the sequence of phonemes with temporal segmentation. The frame-wise network may receive an error or may misalign the segment boundaries, even if it predicts the correct phoneme. When one phoneme always occurs beside another, CTC tends to predict them together in a double spike. The choice of labeling can be read directly from the CTC outputs (follow the spikes), whereas the predictions of the frame-wise network must be post-processed before use. 
     Conventional CTC techniques for use in end-to-end Automatic Speech Recognition (ASR) face some obstacles such as the challenge of incorporating the lexicons and language models into decoding. It has been shown that combining the CTC network with another classifier can improve the performance of the ASR. For example, RNNs for Large Vocabulary Conversational Speech Recognition (LVCSR) trained with CTC can be improved with the level minimum Bayes risk (sMBR) sequence training criterion and approaches the state-of-the-art performance. Despite the promising results obtained using the CTC technique, conventional approaches have several limitations including high computational complexity, unknown latency, and high amount of memory usage which is required to perform the forward-backward algorithm especially when the sequence of training is long. 
     Another approach is described in A. Graves, “Sequence transduction with recurrent neural networks,” in ICML Representation Learning Workshop, 2012, which is incorporated herein by reference in its entirety. As disclosed, the RNN Transducer approach is an extension of the CTC algorithm. Unlike the CTC, which can be seen as an acoustic-only model, the RNN Transducer has another RNN that acts as a language model Similar to the CTC, the probability of observing an output sequence for a given input is computed using the forward-backward algorithm and has similar limitations as other conventional approaches. 
     Conventional systems, such as the systems described herein, have several limitations and disadvantages for practical usage, which are overcome by the systems and methods disclosed herein. First, the CTC network of Graves does not use pre-segmented labeled training data. The embodiments described herein make use of all the information available from the training data to improve the performance of training. Thus, the present disclosure is suitable for speech recognition applications since pre-segmented labeled training data, such as the TIMIT dataset, is widely available. The embodiments disclosed here include solutions which utilize the information of pre-segmented labeled training data to improve the performance. While some conventional algorithms that use labeled training data may be sensitive to alignment errors, the embodiments disclosed herein are not sensitive to the exact alignment of the labels of the input data sequence. As a result, the present disclosure provides a good solution to take the advantage of segmented data even where the labeling alignments are not exactly correct. 
     The computational complexity of the CTC network is high due to the high number of multiplications by taking into consideration all possible paths to compute the signal error. However, the amount of complexity is greatly reduced using the embodiments disclosed herein which include restrictions on the paths that are acceptable according to a region which is called Region of Target (ROT). As a result, a simplified and computationally efficient method is disclosed herein to compute the signal error. 
     In many conventional approaches, the memory requirements and the computational complexity of the CTC method is directly related to the input sequence length. This is because for each input sequence the forward pass will be performed to obtain the network&#39;s outputs for the entire sequence based on the network&#39;s outputs. Forward and backward variables of the same length as the input sequence are typically computed. In contrast to conventional approaches, various embodiments disclosed herein use forward and backwards variables that contain mostly zeroes. This can reduce the computational complexity and it possible to implement a memory efficient trainer using the proposed CTC algorithm. 
     For tasks where segmentation is required (e.g. protein secondary structure prediction), it would be problematic to use a conventional CTC network such as described herein since there is no guarantee that the network prediction would be aligned with the corresponding part of the sequence. On the other hand, the method disclosed herein can solve this problem since it would preserve the alignment of the network prediction. 
     Finally, there is no control on the latency of the conventional CTC algorithms discussed above, which may result in a trained system having unreasonably high latency. In contrast, the methods disclosed herein define a Region of Target (ROT) which includes the whole duration of the phoneme plus the boundary transitions. 
     Referring to  FIGS. 1 and 2 , an embodiment of a system to train a network for a classification application, such as phoneme recognition, will now be described. In a Forward Pass  100  ( FIG. 1 ), the network outputs for all of the output nodes (y k   t ) at the t-th frame of a sequence of training data  108  (comprising a stream of input features computed for a sequence of frames) are computed, where M is the number of classes of phonemes and the network  102  has M+1 nodes at the output layer. The network outputs for all the frames of the sequence are stored in a buffer  105 . 
     In the Backward Pass ( FIG. 2 ), the system starts from the last frame and goes backward in time to compute the signal error for each frame at the input of the softmax node. This error is propagated backwards through the unfolded network, for example, by using the standard Back Propagation Through Time (BPTT) equations as disclosed in A. Graves and J. Schmidhuber, “Framewise phoneme classification with bidirectional long short-term memory (LSTM) and other neural network architectures”, Neural Networks, vol. 18, pp. 602-610, 2005, which is incorporated herein by reference in its entirety. 
     In various embodiments, the backward pass includes the following steps. In step  110 , at each frame, the Region Of Target (ROT) and the forward-backward masking is determined, using information retrieved from the ROT table  120 . The beginning and the end of this region is used to compute the signal error. Using ROT information the forward-backward marking is computed. In step  111 , the modified forward and backward variables are computed. The soft target is computed in step  112  using the modified forward and backward variables and the network outputs. In step  114 , the signal error is computed based on the network outputs and the estimated soft target. Finally, an adaptive learning rate algorithm is used to update the weights and biases of the network in step  116 . 
     The CTC network proposed in Graves, et al., has a softmax output layer with one more output node than there are labels. If the number of labels is M, then the activations of the first M output nodes are interpreted as the probabilities of observing the corresponding labels at particular times. The activation of the extra node is the probability of observing a ‘blank’, or no label. The network outputs define the probabilities of all possible ways of aligning all possible label sequences with the input sequence. The total probability of any one label sequence can then be found by summing the probabilities of its different alignments. The goal of CTC algorithm in Graves, et al., is to maximize the probabilities of the correct labeling. 
     An exemplary operation of an embodiment of a CTC network in accordance with the present invention is illustrated in  FIG. 3 .  FIG. 3  illustrates an example of ROT labeling. As illustrated, an example input in the form of an audio waveform  305  is provided and divided into a sequence of audio frames  310  (e.g., T number of frames in order). In this example, an input sequence including three labels is shown, namely, “h #” (start from t=0 to t=r), “sh” start from t=r to t=r+N−1) and “ix” start from t=r+N−1 to t=L−1). The correct labeling for this example is LABEL=“h #”, “sh”, “ix” and there are many ways that the network can generate this correct labeling through its output. For example, if it is assumed that the total number of frames is T=8, then all of the following outputs are considered to be the correct labeling output (“bl” is blank): 
     “h #”, “sh”, “ix”, “ix”, “ix”, “bl”, “bl”, “bl”→LABEL=“h #”, “sh”, “ix” 
     “h #”, “h #”, “bl”, “bl”, “sh”, “ix”, “ix”, “ix”→LABEL=“h #”, “sh”, “ix” 
     “bl”, “bl”, “bl”, “h #”, “sh”, “ix”, “bl”, “bl”→LABEL “h #”, “sh”, “ix” 
     In this embodiment, the labeling is obtained by removing all blanks and repeated labels from the outputs of the network. Each of the above three examples is considered to be a possible output path of network. As it is clear, all of these three different output paths have the exact same output labels. The following two examples do not produce correct labeling. 
     “h #”, “sh”, “ix”, “bl”, “ix”, “ix”, “bl”, “bl”→LABEL “h #”, “sh”, “ix”, “ix” 
     “h #”, “bl”, “h #”, “bl”, “bl”, “sh”, “ix”, “ix”→LABEL=“h #”, “h #”, “sh”, “ix” 
     In the CTC network of Graves, et al., no restriction on the timing of the network outputs is considered since it is assumed that the data is not segmented. However, training the network with no timing restriction may lead to unreasonable latency. To solve this problem, various embodiments of the present invention apply restrictions to the correct timing for the network outputs. In one embodiment, segmented data such as a TIMIT dataset is available for training. An ROT for each label is defined where the network is expected to spike for that label. As shown in  FIG. 3 , the ROT for label “sh” covers all the frames of “sh” including the transitions on the right and on the left. By defining the ROT to cover the frame and expected transitions, the network is allowed to spike for this label during this region. Next, an embodiment of a CTC algorithm using this ROT restriction is discussed. 
     Compute the Forward-Backward Masking (Step  110 ,  FIG. 2 ) 
     As discussed above, a goal of convention CTC network, such as the network described in Graves, et al., is to maximize the probability of having the correct output labeling by considering all the possible output paths. To allow blanks in the output paths, for each output label such as L=“h #”, “sh”, “ix” we consider a modified label sequence L′=“bl”, “h #”, “bl”, “sh”, “bl”, “ix”, “bl”, with blanks added to the beginning and the end and inserted between every pair of labels as it is shown in  FIG. 4 .  FIG. 4  illustrates a forward backward algorithm applied to a labeled sequence of training data in accordance with an embodiment. As illustrated, black circles represent labels, white circles represent blanks, and arrows signify allowed transitions. Forward variables are updated in the direction of the arrows, and backward variables are updated against them. The length of L′ is therefore 2|L|+1. Also, it is assumed that the length of the sequence for training (the number of frames) is T. 
     All possible output paths are shown by the arrows. In the present embodiment, a goal is to put restrictions on the output paths according to the ROT which is defined in  FIG. 3 . To this end, a forward-backward masking (FBM t (s), t=0, . . . , T−1, s=0, . . . , 2|L|) is defined and applied directly to the forward and backward variables. To obtain FBM t (s), the segmented data information including the timing of the labels is used for each training sequence. This information is stored as the timing information for all the labels, e.g., X time   label ={0, r, r+N−1, T−1}, as shown in  FIG. 3 . In this embodiment, X time   label  stores the transition time information for the labels. For example, X time   label (1)=r is the duration of the first phoneme “h #” in  FIG. 3 . A proposed algorithm to obtain the FBM t (s) using X time   label  information is given below: 
                                        (1)                       FBM   t     ⁡     (   0   )       =     {         1         t   ≤         X   time   label     ⁡     (   1   )       +   f   -   2               0       otherwise         }                                               for   s odd  = 1 to 2 |L| −1 → s odd  =odd number                                 t   start   odd     =     max   ⁡     (           FBM   t     ⁡     (       s   odd     2     )       -   b     ,   0     )                                             t   end   odd     =     min   ⁡     (           FBM   t     ⁡     (         s   odd     2     +   1     )       +   f   -   1     ,     T   -   1       )                                               FBM   t     ⁡     (     s   odd     )       =     {         1           t   start   odd     ≤   t   ≤     t   end   odd               0       otherwise         }                                 s even  = S odd  + 1               t start   even  = t start   odd  + 1                                 t   end   even     =     min   ⁡     (           FBM   t     ⁡     (           s   odd     +   2     2     +   1     )       +   f   -   2     ,     T   -   1       )                                               FBM   t     ⁡     (     s   even     )       =     {         1           t   start   even     ≤   t   ≤     t   end   even               0       otherwise         }                                               end                        
where f depends on the latency of the system and it is set to zero for no-latency system, and b depends on the number of frames backward in the ROT that the network should see to spike for a phoneme. It is desirable that this value not be zero.
 
5.2 Compute the Modified Forward and Backward Variables (Step  111 ,  FIG. 2 )
 
     As it is mentioned above in the discussion of step  110 , the modified label sequence L′ has blanks added to the beginning and the end and inserted between every pair of labels and so it has length equals to 2|L|+1 where |L| is the length of the training label sequence L. The output of the network at the frame t for each symbol of the training label sequence is denoted by y L     t     t . A blank symbol is denoted by y bl   t . 
     After computing the forward-backward masking FBM t (s), the modified forward variable ({circumflex over (α)} t (s)) and the modified backward variable ({circumflex over (β)} t (s)) are computed as follows: 
     1) Forward Variable Calculation in Logarithm Domain 
     Initialization Formula
 
{circumflex over (α)} 0 (0)=log( y   bl   0 )
 
{circumflex over (α)} 0 (1)=log( y   L     0     0 )
 
{circumflex over (α)} 0 ( s )=∞→for  s&gt; 1
 
     Recursion Formula 
                         α   ^     t     ⁡     (   s   )       =     {           A   +     log   ⁡     (     y     L   s   ′     t     )                 if   ⁢           ⁢     mod   ⁡     (     s   ,   2     )         ==     0   ⁢           ⁢   and   ⁢           ⁢       FBM   t     ⁡     (   s   )         ≠   0               B   +     log   ⁡     (     y     L   s   ′     t     )                 if   ⁢           ⁢     mod   ⁡     (     s   ,   2     )         ≠     0   ⁢           ⁢   and   ⁢           ⁢       FBM   t     ⁡     (   s   )         ≠   0             ∞       otherwise         }             (   2   )                 A =log(exp({circumflex over (α)} t−1 ( s ))+exp({circumflex over (α)} t−1 ( s− 1)))
 
 B =log(exp({circumflex over (α)} t−1 ( s ))+exp({circumflex over (α)} t−1 ( s− 1))+exp({circumflex over (α)} t−1 ( s− 2)))
 
2) Backward Variable Calculation in Logarithm Domain
 
     Initialization Formula
 
{circumflex over (β)} T-1 (| L′|− 1)=log( y   bl   T-1 )
 
{circumflex over (β)} T-1 (| L′|− 2)=log( y   L     0     T-1 )
 
{circumflex over (β)} T-1 ( s )=∞→for  s&lt;|L′|− 2
 
     Recursion Formula 
                         β   ^     t     ⁡     (   s   )       =     {           A   +     log   ⁡     (     y     L   s   ′     t     )                 if   ⁢           ⁢     mod   ⁡     (     s   ,   2     )         ==     0   ⁢           ⁢   and   ⁢           ⁢       FBM   t     ⁡     (   s   )         ≠   0               B   +     log   ⁡     (     y     L   s   ′     t     )                 if   ⁢           ⁢     mod   ⁡     (     s   ,   2     )         ≠     0   ⁢           ⁢   and   ⁢           ⁢       FBM   t     ⁡     (   s   )         ≠   0             ∞       otherwise         }             (   3   )                 A =log(exp({circumflex over (β)} t+1 ( s ))+exp({circumflex over (β)} t+1 ( s+ 1)))
 
 B =log(exp({circumflex over (β)} t+1 ( s ))+exp({circumflex over (β)} t+1 ( s+ 1))+exp({circumflex over (β)} t+1 ( s+ 2)))
 
where {circumflex over (α)} t (s) and {circumflex over (β)} t (s) are the modified of forward and backward variables, respectively.
 
Compute the Soft Target (Step  112 ,  FIG. 2 )
 
     The product of the forward and backward variables at a given s and t is the probability of all the paths corresponding to the labeling |L| that go through the symbol s at time t. In logarithm domain, the product is replaced by addition and so the probability of all the paths for |L| is related to the sum of the modified forward and backward variables. As a result, the soft target for each symbol or phoneme k among all the M+1 classes can be computed as follows: 
                       Y   k   ′     =         ∑     s   ∈     lab   ⁡     (     L   ,   k     )           ⁢     exp   ⁡     (           α   ^     i     ⁡     (   s   )       +         β   ^     t     ⁡     (   s   )       -     log   ⁡     (     y     L   s   ′     t     )         )             ∑     s   =   0       2   ⁢          L   ′              ⁢     exp   ⁡     (           α   ^     i     ⁢     (   s   )       +         β   ^     t     ⁡     (   s   )       -     log   ⁡     (     y     L   s   ′     t     )         )             ⁢     
     ⁢       lab   ⁡     (     L   ,   k     )       =     {       s   :     L   s   ′       =   k     }               (   4   )               
Compute Signal Error for all the Output Nodes (Step  114 ,  FIG. 2 )
 
     In this section, an embodiment is disclosed for efficient computation of the signal error at each of the M+1 nodes of the network output prior to applying the softmax nonlinear function. It is assumed that the network output for label k (among M+1 symbols) at frame t before and after softmax nonlinear function are denoted by z k   t , and y k   t , respectively. The relationship between z k   t  and y k   t  are given below: 
     
       
         
           
             
               
                 
                   
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     The computation of the signal error or the objective function derivatives with respect to the unnormalized outputs will now be described with reference to the example in  FIG. 3 . Assume that the signal error at frame t where r≤t≤r+N−1 for symbol k is denoted by δ k   t . As shown in  FIG. 3 , symbol k=“sh” is the target label at frame t (r≤t≤r+N−1). If it is assumed that f, b≠0, then the phoneme duration for label k=“sh” (r≤t≤r+N−1) can be divided to three non-overlapping regions: 
     1) r≤t≤r+f−1→region R1 (“shared region”) 
     2) r+f−1&lt;t≤r+N−b−1→region R2 (“exclusive region”) 
     3) r+N−b−1&lt;t≤r+N−1→region R3 (“shared region”) 
     As shown, R1 is a shared region between phonemes “h #” and “sh” and R3 is a shared region between phonemes “sh” and “ix”. The “h #” or “ix” phonemes are called “shared phoneme” for “sh” and R1 or R3 are called a “shared region”. However, R2 is an exclusive region for phoneme “sh” and so there is no “shared phoneme” in this region and R3 is called an “exclusive region”. Now the signal error at frame t depending it is in the “exclusive region” or not can be computed as follows: 
     a) if frame t is in “shared region”:
 
δ k     target       t     t   =Y   k     target       t     t   −y   k     target       t     t  
 
δ k     target       t     t   =Y   k     target       t     t   −y   k     target       t     t  
 
δ bl   t =(1− Y   k     target       t     t   −Y   k     target       t     t )− y   bl   t  
 
for all other labels→δ k   t   =−y   k   t  
 
     b) if frame t is in “exclusive region”:
 
δ k     target       t     t   =Y   k     target       t     t   −y   k     target       t     t  
 
δ bl   t =1− Y   k     target       t     t   −y   bl   t  
 
for all other labels→δ k   t   =y   k   t  
 
where k target   t  label (here k target   t =“sh”) and k shared   t  is the “shared phoneme” (here it can be k shared   t =“# h” or k shared   t =“ix”).
 
Proposed Adaptive Learning Rate (Step  116 ,  FIG. 2 )
 
     Training the network of the present embodiment may converge too slowly for many applications. In order to improve the performance and increase the training convergence rate, an adaptive learning rate algorithm is utilized. In one embodiment, two different methods are used to update the weights and biases of the network. The weights and biases connected to the output layer are updated according to the following rule for t-th epoch: 
                           ⁢           X   weight     ⁡     (   t   )       =       m   ×       Δ   weight     ⁡     (     t   -   1     )         +     μ   ×       δ   weight     ⁡     (   t   )             ⁢     
     ⁢         Δ   weight     ⁡     (   t   )       =     {             X   weight     ⁡     (   t   )               if   ⁢           ⁢     θ   low   weight       ≤       X   weight     ⁡     (   t   )       ≤     θ   up   weight                   Δ   weight     ⁡     (     t   -   1     )           otherwise         }       ⁢     
     ⁢           ⁢     update   =     {             X   weight     ⁡     (   t   )               if   ⁢           ⁢     θ   low   weight       ≤       X   weight     ⁡     (   t   )       ≤     θ   up   weight               0       otherwise         }       ⁢     
     ⁢           ⁢       weight   ⁡     (   t   )       =       weight   ⁡     (     t   -   1     )       +   update                 (   12   )               
where m=0.9 is the momentum and μ=1e−3 is the learning rate. θ low   weight  and θ up   weight  are the lower bound and upper bound for the update of the weights (or the biases). δ weight (t) is the error signal which is received at the corresponding weights (or biases) using the standard BPTT. For example, for bias of the blank node of the output layer, δ weight (t) equals to δ n   (1)  for n-th frame. As shown in (12), the weights (or the biases) at each epoch are updated using update value. For other weights and biases of the LSTM network, the following rule may be used to update them in accordance with one embodiment:
 
                         initilization   ⁢           ⁢   for   ⁢           ⁢   the   ⁢           ⁢   first   ⁢           ⁢   epoch     →     E   ⁡     (   0   )         =   0     ⁢     
     ⁢       E   ⁡     (   t   )       =       ρ   ×     E   ⁡     (     t   -   1     )         +       (     1   -   ρ     )     ×       (       δ   weight     ⁡     (   t   )       )     2           ⁢     
     ⁢     RMS   =         E   ⁡     (   t   )       +   ɛ         ⁢     
     ⁢         X   weight     ⁡     (   t   )       =       μ   RMS     ⁢       δ   weight     ⁡     (   t   )           ⁢     
     ⁢     update   =     {             X   weight     ⁡     (   t   )               if   ⁢           ⁢     θ   low   weight       ≤       X   weight     ⁡     (   t   )       ≤     θ   up   weight               0       otherwise         }       ⁢     
     ⁢       weight   ⁡     (   t   )       =       weight   ⁡     (     t   -   1     )       +   update               (   13   )               
where ρ=0.95 is a smoothing factor and ε=1e−6 is a fixed constant to avoid infinity. The other parameters are similar to what is discussed above.
 
       FIG. 5  is a flow chart illustrating an embodiment of a high level training process  400  as discussed above. In step  405 , the system receives a sequence of segmented, labeled training data having a plurality of frames. Next, in step  410 , a forward pass process is conducted through the training data to compute the network outputs for all of the output nodes for each frame of the training sequence. A backward pass process is then initiated in step  415  at each frame, identifying a ROT and associated information based on the network outputs. In step  420 , soft targets are estimated using the ROT information and the network outputs. The signal error for all output nodes is calculated in step  425 . Finally, in step  430 , an adaptive learning rate algorithm is applied to update the weights and biases of the network. 
       FIG. 6  illustrates an exemplary classification training system  500  of some embodiments that performs at least part of the classification training process described above. The classification training system  500  is programmed to perform the training processes described above. The classification training system includes the training dataset  537  which contain the pre-segmented labeled training data. Training dataset  537  is connected to the classification training module  540  which includes the ROT module  544 . The ROT module builds an ROT table using information about the segmented training data provided by training dataset  537 . The classification training module  540  also includes a forward pass processing module  541  programmed to perform the forward pass process described above and a backward pass processing module  542  programmed to perform the backward pass processes described above. The adaptive learning module  543  includes one or more adaptive learning algorithms that can be used by the backward pass processing module to update the weights and biases of a neural network, such as the LSTM network, as described above. In some of these embodiments, the classification training module  540  can iteratively perform the training processes using different training data to continuously improve and update the neural network. The classification training module  540  can store the updated neural network in the memory  528 . The processor  520 , can be a micro-controller, a digital signal processor (DSP), or other processing components, for controlling and facilitating the operations of the classification training system  500 , including controlling communications with internal and external devices. The classification training system  500  further includes one or more communication channels such as a bus for facilitating communication of data between various components of the classification system  500 . Components may include device modules  525  for providing device operation and functionality, which may include input/output components  526  such as a touch screen, keyboard, mouse, etc., a display  527  such as a monitor, a memory  528  such as RAM, solid state drive, disk drive, database, etc., and a communications interface  529 . In some embodiments, the communications interface  529  may include a network interface (e.g., Ethernet interface) or a wireless transceiver for enabling the classification system to communicate with remote devices over a network. In operation, training of the neural network is performed by classification training system  500  offline and the trained model including the weights and biases of the neural network will be stored in Classification system  600 . 
       FIG. 7  illustrates an exemplary classification system  600  of some embodiments that performs at least part of the classification training process described above. The classification system  600  may be implemented as a mobile device, such as a smart phone or a laptop computer, a television or display monitor, a display computer, a computer server, an automobile, a speech recognition system, or any other device that provides audio keywords (e.g., commands) recognition capability. The classification system  600  is communicatively coupled with one or more audio inputting devices  605  such as a microphone and optionally also with one or more audio outputting devices  610  such as a loudspeaker. 
     In some embodiments, the classification system  600  can include an analog-to-digital converter  615  that converts the analog audio signals received from the audio inputting devices  605  into digital audio signals and sends the digital audio signals to processor  620 , which can be a micro-controller, a digital signal processor (DSP), or other processing components, for controlling and facilitating the operations of the classification system  600 , including controlling communications with internal and external devices. The classification system  600  may also include a digital-to-analog converter  650  that converts digital audio signals generated by the different modules and components of the classification system  600  to analog signals before transmitting the analog signals to the one or more audio outputting devices  610 . 
     The classification system  600  includes one or more communication channels such as a bus for facilitating communication of data between various components of the classification system  600 . Components may include device modules  625  for providing device operation and functionality, which may include input/output components  626  such as a touch screen, keyboard, mouse, etc., a display  627  such as a monitor, a memory  628  such as RAM, solid state drive, disk drive, database, etc., and a communications interface  629 . In some embodiments, the communications interface  629  may include a network interface (e.g., Ethernet interface) or a wireless transceiver for enabling the classification system to communicate with remote devices over a network  632 . Remote devices may include user devices  630  (e.g., household appliances, other user electronics, etc.), or a web server  635  that is communicatively coupled with a media storage  637 . 
     The classification system  600  also includes a classification module  645  programmed to use the neural network that was trained and updated by the classification training system  500 . The classification module includes the forward pass processing module  644 . In one embodiment, Forward Pass Processing Module  644  is programmed to perform classification and prediction on audio input data received from audio inputting devices  605 . In various embodiments, classification module  645  may include an automatic speech recognition (ASR) module providing voice command processing, an image search and classification system, an object detection system, medical diagnostic module, or other application configured for use with the neural network as described herein. The forward pass processing module  644  can generate a response based on a LSTM network in real-time or close to real-time of the audio input. In some embodiments, the classification module  645  can be programmed to output the response as an audio sound via the digital-to-analog converter  650  and the audio outputting devices  610 . Instead of or in addition to producing and audio signal, the classification module  645  can be programmed to send a signal to an external device (e.g., to initiate an action or a transaction through the external device) based on the generated response. For example, the classification system  600  can be part of or communicatively coupled with a smart home system, and send a signal to a user device (e.g., a household appliance) via a network  632  (e.g., a local area network within the user&#39;s residence) based on the generated response (e.g., sending a signal to turn on the television based on the response generated by an audio input from the user). In another example, the classification system  600  can initiate a transaction with a webserver  635  over the Internet based on the generated response (e.g., sending a signal to the web server  635  to purchase a movie stored in media storage  637  based on the response generated by an audio input from the user). The classification system disclosed herein is not limited to processing audio signals, but can be used to train a neural network to process different input (e.g., image data, video data, etc.) as well. 
     It should be noted that any language directed to a computer should be read to include any suitable combination of computing devices, including servers, interfaces, systems, databases, agents, peers, engines, modules, controllers, or other types of computing devices operating individually or collectively. One should appreciate the computing devices comprise a processor configured to execute software instructions stored on a tangible, non-transitory computer readable storage medium (e.g., hard drive, solid state drive, RAM, flash, ROM, etc.). The software instructions preferably program the computing device to provide the roles, responsibilities, or other functionality as discussed above with respect to the disclosed apparatus. In especially preferred embodiments, the various servers, systems, databases, or interfaces exchange data using standardized protocols or algorithms, possibly based on Hypertext Transfer Protocol (HTTP), Hypertext Transfer Protocol Secure (HTTPS), Advanced Encryption Standard (AES), public-private key exchanges, web service application program interfaces (APIs), known financial transaction protocols, or other electronic information exchanging methods. Data exchanges preferably are conducted over a packet-switched network, the Internet, local area network (LAN), wide area network (WAN), virtual private network (VPN), or other type of packet switched network. 
     Where applicable, various embodiments provided by the present disclosure may be implemented using hardware, software, or combinations of hardware and software. Also, where applicable, the various hardware components and/or software components set forth herein may be combined into composite components comprising software, hardware, and/or both without departing from the spirit of the present disclosure. Where applicable, the various hardware components and/or software components set forth herein may be separated into sub-components comprising software, hardware, or both without departing from the scope of the present disclosure. In addition, where applicable, it is contemplated that software components may be implemented as hardware components and vice-versa. 
     Software, in accordance with the present disclosure, such as program code and/or data, may be stored on one or more computer readable mediums. It is also contemplated that software identified herein may be implemented using one or more general purpose or specific purpose computers and/or computer systems, networked and/or otherwise. Where applicable, the ordering of various steps described herein may be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein. 
     The foregoing disclosure is not intended to limit the present disclosure to the precise forms or particular fields of use disclosed. As such, it is contemplated that various alternate embodiments and/or modifications to the present disclosure, whether explicitly described or implied herein, are possible in light of the disclosure. Having thus described embodiments of the present disclosure, persons of ordinary skill in the art will recognize that changes may be made in form and detail without departing from the scope of the present disclosure. Thus, the present disclosure is limited only by the claims.