1.6 Continuous Speech Recognition

 

Returning now to the conceptual model of speech production and recognition exemplified by Fig. 1.1, it should be clear that the extension to continuous speech simply involves connecting HMMs together in sequence. Each model in the sequence corresponds directly to the assumed underlying symbol. These could be either whole words for so-called connected speech recognition or sub-words such as phonemes for continuous speech recognition. The reason for including the non-emitting entry and exit states  should now be evident, these states provide the glue needed to join models together.

There are, however, some practical difficulties to overcome. The training data for continuous speech must consist of continuous utterances and, in general, the boundaries dividing the segments of speech corresponding to each underlying sub-word model in the sequence will not be known. In practice, it is usually feasible to mark the boundaries of a small amount of data by hand. All of the segments corresponding to a given model can then be extracted and the isolated word style of training described above can be used. However, the amount of data obtainable in this way is usually very limited and the resultant models will be poor estimates. Furthermore, even if there was a large amount of data, the boundaries imposed by hand-marking may not be optimal as far as the HMMs are concerned. Hence, in HTK the use of HINIT and HREST for initialising sub-word models is regarded as a bootstrap  operation. The main training phase involves the use of a tool called HEREST  which does embedded training.

Embedded training  uses the same Baum-Welch procedure as for the isolated case but rather than training each model individually all models are trained in parallel. It works in the following steps:

1. Allocate and zero accumulators for all parameters of all HMMs.
2. Get the next training utterance.
3. Construct a composite HMM by joining in sequence the HMMs corresponding to the symbol transcription of the training utterance.
4. Calculate the forward and backward probabilities for the composite HMM. The inclusion of intermediate non-emitting states in the composite model requires some changes to the computation of the forward and backward probabilities but these are only minor. The details are given in chapter 8.
5. Use the forward and backward probabilities to compute the probabilities of state occupation at each time frame and update the accumulators in the usual way.
6. Repeat from 2 until all training utterances have been processed.
7. Use the accumulators to calculate new parameter estimates for all of the HMMs.

These steps can then all be repeated as many times as is necessary to achieve the required convergence. Notice that although the location of symbol boundaries in the training data is not required (or wanted) for this procedure, the symbolic transcription of each training utterance is needed.

Whereas the extensions needed to the Baum-Welch procedure for training sub-word models are relatively minor, the corresponding extensions to the Viterbi algorithm are more substantial.

In HTK, an alternative formulation of the Viterbi algorithm is used  called the Token Passing Model . In brief, the token  passing model makes the concept of a state alignment path explicit. Imagine each state j of a HMM at time t holds a single moveable token which contains, amongst other information, the partial log probability . This token then represents a partial match between the observation sequence to and the model subject to the constraint that the model is in state j at time t. The path extension algorithm represented by the recursion of equation 1.31 is then replaced by the equivalent token passing algorithm which is executed at each time frame t. The key steps in this algorithm are as follows

1. Pass a copy of every token in state i to all connecting states j, incrementing the log probability of the copy by .
2. Examine the tokens in every state and discard all but the token with the highest probability.

In practice, some modifications are needed to deal with the non-emitting states but these are straightforward if the tokens in entry states are assumed to represent paths extended to time and tokens in exit states are assumed to represent paths extended to time .

The point of using the Token Passing Model is that it extends very simply to the continuous speech case. Suppose that the allowed sequence of HMMs is defined by a finite state network. For example, Fig. 1.7 shows a simple network in which each word is defined as a sequence of phoneme-based HMMs and all of the words are placed in a loop. In this network, the oval boxes denote HMM instances  and the square boxes denote word-end nodes . This composite network is essentially just a single large HMM and the above Token Passing algorithm applies. The only difference now is that more information is needed beyond the log probibility of the best token. When the best token reaches the end of the speech, the route it took through the network must be known in order to recover the recognised sequence of models.

 

The history of a token's route through the network may be recorded efficiently as follows. Every token carries a pointer called a word end link. When a token is propagated from the exit state of a word (indicated by passing through a word-end node ) to the entry state of another, that transition represents a potential word boundary. Hence a record called a Word Link Record is generated   in which is stored the identity of the word from which the token has just emerged and the current value of the token's link. The token's actual link is then replaced by a pointer to the newly created WLR. Fig. 1.8 illustrates this process.

Once all of the unknown speech has been processed, the WLRs attached to the link of the best matching token (i.e. the token with the highest log probability) can be traced back to give the best matching sequence of words. At the same time the positions of the word boundaries can also be extracted if required.

 

The token passing algorithm for continuous speech has been described in terms of recording the word sequence only. If required, the same principle can be used to record decisions at the model and state level. Also, more than just the best token at each word boundary can be saved. This gives the potential for generating a lattice of hypotheses rather than just the single best hypothesis. Algorithms based on this idea are called lattice N-best . They are suboptimal because the use of a single token per state limits the number of different token histories that can be maintained. This limitation can be avoided by allowing each model state to hold multiple-tokens and regarding tokens as distinct if they come from different preceding words. This gives a class of algorithm called word N-best which has been shown empirically to be comparable in performance to an optimal N-best algorithm.   

The above outlines the main idea of Token Passing as it is implemented within HTK. The algorithms are embedded in the library modules HNET  and HREC  and they may be invoked using the recogniser tool called HVITE . They provide single and multiple-token  passing recognition, single-best output, lattice output, N-best lists, support for cross-word context-dependency, lattice rescoring  and forced alignment .