- freely available
Symmetry 2019, 11(8), 1018; https://doi.org/10.3390/sym11081018
2. Background of Automatic Speech Recognition
2.1. History of ASR
2.2. Models for LVCSR
2.2.1. HMM-Based Model
- Acoustic model : It indicates the probability of observing X from hidden sequence S. According to the probability chain rule and the observation independence hypothesis in HMM (observations at any time depend only on the hidden state at that time), can be decomposed into the following form:
- Pronunciation model : this is also called the dictionary. Its role is to achieve the connection between acoustic sequence and language sequence. The dictionary includes various levels of mapping, such as pronunciation to phone, phone to trip-hone. The dictionary is not only used to achieve structural mapping, but also to map the probability calculation relationship.
- Language model : is trained by a large amount of corpus, using order Markov hypothesis to generate a m-gram language model.
- The training process is complex and difficult to be globally optimized. HMM-based model often uses different training methods and data sets to train different modules. Each module is independently optimized with their own optimization objective functions which are generally different from the true LVCSR performance evaluation criteria. So the optimality of each module does not necessarily mean the global optimality [21,22].
- Conditional independent assumptions. To simplify the model’s construction and training, the HMM-based model uses conditional independence assumptions within HMM and between different modules. This does not match the actual situation of LVCSR.
2.2.2. End-to-End Model
- Multiple modules are merged into one network for joint training. The benefit of merging multiple modules is that there is no need to design many modules to realize the mapping between various intermediate states . Joint training enables the end-to-end model to use a function that is highly relevant to the final evaluation criteria as a global optimization goal, thereby seeking globally optimal results .
- It directly maps input acoustic signature sequence to the text result sequence, and does not require further processing to achieve the true transcription or to improve recognition performance , whereas in the HMM-based models, there is usually an internal representation for pronunciation of a character chain. Nevertheless, character level models are known with some traditional architectures, too.
- CTC-based: CTC first enumerates all possible hard alignments (represented by the concept path), then it achieves soft alignment by aggregating these hard alignments. CTC assumes that output labels are independent of each other when enumerating hard alignments.
- RNN-transducer: it also enumerates all possible hard alignments and then aggregates them for soft alignment. But unlike CTC, RNN-transducer does not make independent assumptions about labels when enumerating hard alignments, so it is different from CTC in terms of path definition and probability calculation.
- Attention-based: this method no longer enumerates all possible hard alignments, but uses Attention mechanism to directly calculate the soft alignment information between input data and output label.
3. CTC-Based End-to-End Model
- Data alignment problem. CTC no longer needs to segment and align training data. This solves the alignment problem so that DNN can be used to model time-domain features, which greatly enhances DNN’s role in LVCSR tasks.
- Directly output the target transcriptions. Traditional models often output phonemes or other small units, and further processing is required to obtain the final transcriptions. CTC eliminates the need for small units and direct output in final target form, greatly simplifying the construction and training of end-to-end model.
3.1. Key Ideas of CTC
3.1.1. Path Probability Calculation
3.1.2. Path Aggregation
- Merge the same contiguous labels. If consecutive identical labels appear in the path, merge them and keep only one of them. For example, for two different paths “c-aa-t-” and “c-a-tt-”, they are aggregated according to the above principles to give the same result: “c-a-t-”.
- Delete the blank label “-” in the path. Since label “-” indicates that there is no output, it should be deleted when the final label sequence is generated. The above sequence “c-a-t-”, after being aggregated according to the present principle, becomes final sequence “cat”.
3.2. CTC-Based Works
3.2.1. Model Structure
3.2.2. Large-Scale Data Training
3.2.3. Language Model
4. RNN-Transducer End-to-End Model
- CTC cannot model interdependencies within the output sequence because it assumes that output elements are independent of each other. Therefore, CTC cannot learn the language model. The speech recognition network trained by CTC should be treated as only an acoustic model.
- CTC can only map input sequences to output sequences that are shorter than it. For scenarios where output sequence is longer, CTC is powerless. This can be easily analyzed from CTC’s calculation process.
4.1. Key Ideas of RNN-Transducer
- Transcription network (): this is the encoder, which plays the role of an acoustic model. Regardless of sub sampling technique, for an acoustic input sequence of length T, map it to a feature sequence . Correspondingly, for input value at any time t, Transcription network is used as an acoustic model with an output value , which is a dimensional vector.
- Prediction network (): It’s part of the Decoder that plays the role of language model. As an RNN network, it models the interdependencies within output label sequence (while transcription network models the dependencies within acoustic input). maintains a hidden state and an output value for any label location . Their loop calculation process is
- Joint network (): it does the alignment job between input and output sequence. For any , , the joint network uses the transcription network’s output and the prediction network’s output to calculate the label distribution at output location u:
- Since one input data can generate a label sequence of arbitrary length, theoretically, the RNN-transducer can map input sequence to an output sequence of arbitrary length, whether it is longer or shorter than the input.
- Since the prediction network is an RNN structure, each state update is based on previous state and output labels. Therefore, the RNN-transducer can model the interdependence within output sequence, that is, it can learn the language model knowledge.
- Since Joint Network uses both language model and acoustic model output to calculate probability distribution, RNN-Transducer models the interdependence between input sequence and output sequence, achieving joint training of language model and the acoustic model.
4.2. RNN-Transducer Works
- Experiments show that the RNN-transducer is not easy to train. Therefore, it is necessary for each part to be pre-trained, which is very important for improving performance.
- Thd RNN-transducer’s calculation process includes many obviously unreasonable paths. Even if there are some improving works, it is still unavoidable. In fact, all speech recognitions that first enumerate all possible paths and then aggregate them face this problem, including CTC.
5. Attention-Based End-to-End Model
- Speech recognition is also a sequence-to-sequence process that recognizes the output sequence from the input sequence. So it is essentially the same as translation task.
- The encoder–decoder method using an attention mechanism does not require pre-segment alignment of data. With attention, it can implicitly learn the soft alignment between input and output sequences, which solves a big problem for speech recognition.
- Encoding result is no longer limited to a single fixed-length vector, the model can still have a good effect on long input sequence, so it is also possible for such model to handle speech input of various lengths.
5.1. Works on the Encoder
5.1.1. Delay and Information Redundancy
5.1.2. Network Structure
5.2. Works on Attention
- Context-based: it uses only the input feature sequence F and the previous hidden state to calculate the weight at each position. Ref.  used this method. Its problem is that it does not use position information. For a feature’s different appearances in the feature sequence, context-based attention will give them same weights, which is called similarity speech fragment problem.
- Location-based: the previous weight is utilized as location information at each step to calculate current weight . But since it does not use the input feature sequence F, it is not sufficient for input features.
- Hybrid: as the name implies, it takes into account the input feature sequence F, the previous weight , and the previous hidden state , which enables it to combine advantages of context-based and location-based attention.
5.2.1. Continuity Problem
5.2.2. Monotonic Problem
5.2.3. Inaccurate Extraction of Key Information
- In the weights calculated by softmax, every item is greater than zero, which means that every feature of the feature sequence will be retained in the context information obtained by attention. This introduces unnecessary noise characteristics and retains some information that should not be retained in .
- Softmax itself concentrates the probability on the largest item, causing attention to focus mostly on one feature, while other features that have positive effects and should be retained are not fully preserved.
- It introduced a scale value greater than unity in softmax operation, which can enlarge greater weight and shrink smaller one. This method can attenuate the problem of noise characteristics, but makes the probability distribution more concentrated to the feature items with higher probability.
- After having calculated the weight of softmax, it only retains the largest k values and then normalizes them again. This method increases the amount of attention calculation.
- Window method. A calculation window and a window sliding strategy are set in advance, and attention’s weights are calculated by softmax only for data in the window.
5.3. Works on Decoder
6. Comparison and Conclusions
6.1. Model Characteristics Comparison
- Delay: the forward-backward algorithm used by CTC can adjust the probability of decoding results immediately after receiving a encoding result without waiting for other information, so CTC-based model has the shortest delay. RNN-Transducer model is similar to CTC in that it also adjusts the probability of decoding results immediately after each acquisition of an encoding result. However, since it uses not only the encoding result but also the previous decoding output in the forward-backward calculation at each step, its delay is higher compared to CTC. But, they all support streaming speech recognition, where recognition results are generated the same time speech signals are produced. Attention-based model has the longest delay because it needs to wait until all the encoding results are generated before soft alignment and decoding can start. So streaming speech recognition is not supported unless some corresponding compromise is made.
- Computational complexity: In the process of calculating the probability of L of length N based on X of length T, CTC technology needs to do a softmax on each input time step with a complexity of . RNN-transducer technology requires a softamx operation on each input/output pair with a complexity of . The primitive attention, like RNN-transducer, also requires a softamx operation on each input/output pair with complexity. However, as more and more attention mechanism uses window mechanisms, attention-based model only needs to perform softmax operation on the input segments in the window when outputting each label, and its complexity is reduced to , .
- Language modeling capabilities: CTC assumes that output labels are independent of each other, so it has no ability to model language. RNN-transducer enables models with language model ability through the joint network, while attention-based models achieves this through decoder network.
- Training difficulty: CTC is essentially a loss function, and there is no weight to train in itself. Its role is to train an acoustic model. Therefore, the CTC-based network model can quickly achieve optimal results. Attention mechanism and the joint network in RNN-transducer model introduce weights and functions that require training, and because of the joint training of language model and acoustic model, they are much more difficult to train than CTC-based models. In addition, RNN-transducer’s joint network will generate a lot of unreasonable data alignment between input and output. So it is more difficult to train than attention-based models. Generally, it needs to pre-train the prediction network and transcription network to have better performance.
- Recognition accuracy: due to the modeling of linguistic knowledge, recognition accuracies of RNN-Transducer and attention-based model are much higher than that of CTC-based model. In most cases, the accuracy of attention-based model is higher than RNN-transducer.
6.2. Model Recognition Performance Comparison
- Without external language model, CTC-based model has the worst effect, and the gap between it and other models is very large. This is consistent with our analysis and expectation of CTC: it makes a conditional independent assumption of the output and cannot learn language model knowledge itself.
- Compared with CTC, RNN-transducer has been greatly improved on all test sets because compared to CTC, it uses Prediction network to learn language knowledge.
- Attention-based model is the best of all end-to-end, and the two-layer decoder is better than the one-layer Decoder. This shows that decoder’s depth also has an impact on the results.
7. Future Works
- Model delay. CTC-based and RNN-transducer models are monotonic and support streaming decoding, so they are suitable for online scenarios with low latency. However, their recognition performance is limited. Attention-based models can effectively improve the recognition performance, but it is not monotonous and has long delay. Although there are methods such as “window” to reduce the delay of attention, they may reduce the recognition performance to a certain extent . Therefore, reducing latency while ensuring performance is an important research issue for the end-to-end model.
- Language knowledge learning. HMM-based model uses additional language models to provide a wealth of language knowledge, while all the language knowledge of the end-to-end model comes only from training data’s transcriptions, whose coverage is very limited. This leads to great difficulties in dealing with scenes with large linguistic diversity. Therefore, the end-to-end model needs to improve its learning of language knowledge while maintaining the end-to-end structure.
Conflicts of Interest
|LPC||Linear predictive coding|
|TIMIT||Texas Instruments, Inc. and the Massachusetts Institute of Technology|
|PER||Phoneme error rate|
|WSJ||Wall street journal|
|HMM||Hidden Markov model|
|GMM||Gaussian mixed model|
|DNN||Deep neural network|
|CTC||Connectionist temporal classification|
|RNN||Recurrent neural network|
|ASR||Automatic speech recognition|
|LVCSR||Large vocabulary continuous speech recognition|
|CNN||Convolutional neural network|
|LSTM||Long short time memory|
|GPU||Graphics processing unit|
|OpenMPI||Open message passing interface|
|WER||Word error rate|
|LAS||Listen, attend and spell|
|CDF||Cumulative distribution function|
|BLSTM||Bi-directional long short time memory|
|GRU||Gated recurrent unit|
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|RNN Trans.with att.||6.5||12.5||8.4||21.5||9.7|
|Att. 1-layer dec.||6.6||11.7||8.7||20.6||9.0|
|Att. 2-layer dec.||6.3||11.2||8.1||19.7||8.7|
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