Development of End-to-End Models

Contributors: YiningLei, Xinyi Ma, Qing Li, Jingwen Shi

Introduction

The development of end-to-end models represent a significant shift in the field of automatic speech recognition (ASR), which seek to simplify the complex pipeline of traditional systems by directly mapping input audio sequence to sequence of words or other graphemes. Framed in the deep learning context and taking advantage of Neural Network(NN) architectures, these models directly capture the acoustic and linguistic information present in the speech signal, casting a possibly complex processing pipeline into the coherent and flexible modeling language of neural networks. The functional structure of end-to-end models is shown below:

L = {$l_{1}$,···，$l_{n}$}  output sequence

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Decoder

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Aligner

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F = {$f_{1}$,···，$f_{T}$}  feature sequence

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Encoder

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X = {$x_{1}$,···，$x_{T}$}  input sequence

There are several major advantages of end-to-end models over traditional hybrid models. First, end-to-end models use a single objective function which is consistent width the ASR objective to optimize the whole network, while traditional hybrid models optimize individual components separately, which cannot guarantee the global optimum. Second, end-to-end models perform well without deep knowledge about the problem, despite its complexity, by using a unified Neural Network architecture and an appropriate learning algorithm for Natural Language Processing (NLP), task-specific engineering and lots of prior knowledge required in traditional hybrid models can be avoided. Third, because a single network is used for ASR, end-to-end models are much more compact than traditional hybrid models. Therefore, end-to-end models has the potential to improve accuracy and efficiency in various applications, including voice assistants, transcription services, and more.

Historical Context

In 2006, Alex Graves and his colleagues introduced Connectionist Temporal Classification (CTC), which is considered a precursor to end-to-end models. The CTC loss function allows for training deep neural networks end-to-end for tasks like ASR. The previously unavoidable task of segmenting the sound into chunks representing words or phones was now redundant.

From Late 2000s to 2010s, deep learning brings remarkable improvements in many researches, speech recognition also gain development in this boom. In 2011, Yu Dong, Deng Li, etc. from Microsoft Research Institute proposed a hidden Markov model combined with context-based deep neural network which named context-dependent (CD)-DNN-HMM . It achieved significant performance gains compared to traditional HMM-GMM system in large vocabulary continuous speech recognition task. Since then, LVCSR technology using deep learning has begun to be widely studied.

LVCSR can be divided into two categories: HMM-based model and the end-to-end model. In the HMM-based model, different modules use different technologies and play different roles. HMM is mainly used to do dynamic time warping at the frame level. GMM and DNN are used to calculate HMM hidden states’ emission probability. The construction process and working mode of the HMM-based model determines if it faces the following difficulties in practical use: First, 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 . Second, 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, which does not match the actual situation of LVCSR. Due to the above-mentioned shortcomings of the HMM-based model, coupled with the promotion of deep learning technology, Researchers began exploring end-to-end models as an alternative to traditional systems.

In 2014, Baidu's Deep Speech, led by Andrew Ng's team, demonstrated the potential of end-to-end models for LVCSR. Their Deep Speech system used deep neural networks (DNNs) to map audio to text directly. In 2015, a breakthrough came with the introduction of the Listen, Attend, and Spell (LAS) model by Chan and his colleagues from Google Brain and Carnegie Mellon University. LAS used an attention mechanism to improve sequence-to-sequence mapping for automatic speech recognition (ASR) tasks. Since then Attention mechanisms, originally developed for machine translation, have become a critical component of end-to-end ASR models. In 2018, Vaswani and his colleagues first introduced Transformer , have revolutionized various natural language processing tasks, in which speech recognition was included. Models like Conformer and ESPnet's Transformer ASR have achieved state-of-the-art results in ASR tasks, since then, end-to-end ASR models have been adopted by major technology companies, including Google, Amazon, and Microsoft, for their voice assistants and transcription services.

Key Innovations

Significant Shift : Simplifying Speech Recognition

End-to-end modeling can directly translate the speech input into the output only using a single neural network, unlike the traditional one, which has several independent elements. In traditional ASR, the majority of ASR systems comprise distinct acoustic, pronunciation, and language model components, each trained separately. The creation of a pronunciation lexicon and the specification of phoneme sets for a specific language necessitate expertise and are time-intensive tasks. ([1]) shows its structure.

E2E (Click here to view the end-to-end models workflow) speech recognition significantly simplifies the complexity of traditional models. Manual labeling of information is unnecessary, as the neural network can autonomously learn language and pronunciation details.

1. Connectionist Temporal Classification (CTC)

As a technique to train an acoustic model without the need for precise frame-level alignments. Initially, using CTC to generate target phonemes didn't truly constitute an end-to-end method, as it still relied on language models. CTC allows for training an acoustic model without the necessity of frame-level alignments aligning acoustic data with the transcriptions.

The utilization of CTC as the loss function in training the acoustic model represents an end-to-end training approach. This method eliminates the necessity for prior data alignment, requiring only an input sequence and an output sequence for training. Consequently, manual alignment and labeling of data become unnecessary. Moreover, CTC's direct output sequence prediction doesn't require external post-processing. Within the CTC framework, 'blank' is introduced (denoting no predicted value for a given frame). Each prediction's classification corresponds to a spike in the speech waveform, while non-spike regions are considered 'blank.' In a speech sequence, CTC ultimately generates a spike sequence, irrespective of each phoneme's duration.

2. Recurrent Neural Network (RNN)

RNN-transducer lists all potential rigid alignments and aggregates them for achieving flexible alignment. However, unlike CTC, RNN-transducer does not assume label independence during the enumeration of rigid alignments. Consequently, it differs from CTC in terms of how paths are defined and probabilities are calculated.

3. Attention Mechanism

Attention-based Encoder-Decoder Models made their initial appearance within the domain of neural machine translation. The primary purpose of the Attention Mechanism is to address issues present in traditional RNN-based sequence-to-sequence models. This approach takes a different route by foregoing the enumeration of all potential rigid alignments. Instead, it employs the Attention mechanism to directly get soft alignment details between the input data and output labels.

Advantage of End-to-End Models ASR

Initially, end-to-end models models simplify the ASR pipeline substantially by directly generating characters or even words. Conversely, the design of traditional hybrid models is intricate, demanding extensive expertise and years of ASR experience.

Furthermore, the utilization of a single network for ASR makes end-to-end models models significantly more compact compared to traditional hybrid models. This compactness enables easy deployment of end-to-end models models on high-accuracy devices.

Last, end-to-end models has a much simpler training approach with models, which reduces learning time, decoding time, and allows joint optimization with subsequent processing, such as understanding the natural language.

Impact

1.The Impact on the Field of Speech Recognition

Simplify Processes and Reduce Labor Costs:

The end-to-end model eliminates the need for multiple processing steps and manual feature engineering, reducing the time and effort to develop and maintain the model. In the aspect of speech recognition, the end-to-end model simplifies the architecture of the speech recognition system, eliminating the need for complex hand-designed feature extractors or acoustic models.

Better Performance:

End-to-end models can extract relevant features directly from the original data, sometimes better than traditional processes to capture the complex relationships of the data, so the performance of the model may be improved. The end-to-end model can better capture the complex relationship between audio and text, which usually enables speech recognition to achieve higher recognition accuracy than traditional methods.

More Extensive Applications:

The end-to-end model can be applied to a variety of tasks, such as speech recognition, machine translation, image processing and so on, making the solutions of these tasks easier to implement and popularize. For speech recognition, this simplified method can be easily applied to a variety of speech recognition tasks, including multiple languages, accents from all over the world, language habits of various cultural backgrounds and speech recognition in specific fields.

Faster Experimental Iteration:

The simplified modeling process enables researchers to try new ideas and algorithms faster and faster, accelerating research progress. The end-to-end model provides more flexibility and innovative possibilities for researchers in the field of speech recognition.

More Efficient Training:

End-to-end models may require more data to train, and more computer resources to process and train these large models. But at the same time, end-to-end training can provide more efficient training for speech recognition, and direct training from original audio to text can make more efficient use of a large number of tagged data and improve the performance of the model.

Listen, Attend and Spell (LAS)

Listen, Attend and Spell (LAS), is an attention-based neural network that can di-rectly transcribe acoustic signals to characters. LAS is based on the sequence to sequence framework with a pyramid structure in the encoder that reduces the number of timesteps that the decoder has to attend to. LAS is trained end-to-end and has two main components: a listener and a speller. The listener is a pyramidal recurrent net- work encoder that accepts filter bank spectra as inputs. The speller is an attention- based recurrent network decoder that emits characters as outputs. The network produces character sequences without making any independence assumptions be- tween the characters. This is the key improvement of LAS over previous end-to-end CTC models. On a subset of the Google voice search task, LAS achieves a word error rate (WER) of 14.1% without a dictionary or a language model, and 10.3% with language model rescoring over the top 32 beams. By comparison, the state-of-the-art CLDNN-HMM model achieves a WER of 8.0%.

2.The Impact of Its Applications in Various Industries

Transformer

Transformer, is the first sequence transduction model based entirely on attention, replacing the recurrent layers most commonly used in encoder-decoder architectures with multi-headed self-attention. End-to-end memory networks are based on a recurrent attention mechanism instead of sequence-aligned recurrence and have been shown to perform well on simple-language question answering and language modeling tasks. For translation tasks, the Transformer can be trained significantly faster than architectures based on recurrent or convolutional layers. On both WMT 2014 English-to-German and WMT 2014 English-to-French translation tasks our best model outer forms even all previously reported ensembles.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

Region Proposal Network (RPN) shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. Adaptively-sized pooling (SPP) on shared convolutional feature maps is developed for efficient region-based object detection, and semantic segmentation. Fast R-CNN enables end-to-end detector training on shared convolutional features and shows compelling accuracy and speed.

Future research

Evolving end-to-end speech recognition modeling heralds a promising path of exploration and innovation. This section will cover several emerging directions, including issues ranging from robustness in unfavorable acoustic environments to the ethical and privacy considerations inherent in a wide range of applications. In addition, they include the quest for enhanced adaptability, the pursuit of multimodal integration, and the relentless pursuit of more accurate and contextualized transcription. Adoption of these future studies will form the basis for the next phase of end-to-end speech recognition paradigm innovation.

Robustness in Noisy Acoustic Environments:

An overarching issue in ASR research is how to strengthen models from the deleterious effects of acoustic perturbations, especially in real-world scenarios characterized by external noise sources. Research efforts could focus on techniques to improve the adaptability of end-to-end ASR systems to various ambient acoustic environments.

Low-Resource and Under-Resourced Linguistic Contexts:

Due to the lack of annotated data for training automatic recognition models for marginalized or low-resource dialects of languages, future research efforts may turn to methods for improving automatic recognition performance in the domain of resource-scarce languages. In addition to unsupervised or semi-supervised learning frameworks, this may include the judicious use of transfer learning paradigms for resource-rich languages.

Multimodal Convergence:

The confluence of diverse modalities, such as audio, visual, and textual cues, embodies an incipient frontier in ASR research. Forward-looking investigations may be focused on the conceptualization and realization of end-to-end models adept at assimilating and fusing information gleaned from these heterogeneous sources to engender a more holistic comprehension of the targeted input.

Ethical and Privacy Implications:

As ASR technology becomes more widespread, ethical considerations are increasing. Research efforts may include the development of privacy-protective ASR methods, as well as ethical issues related to data collection, minimizing bias, and ensuring fairness in the operation of ASR systems.

Interpretability and Explicability of Models:

Interrogating the rationales underpinning model predictions constitutes an essential endeavor, particularly in high-stakes contexts such as healthcare or legal transcription. Future research might orient itself toward the development of techniques that render end-to-end ASR models more amenable to interpretability.

Zero-Shot and Few-Shot Learning Paradigms:

Investigation into the development of models capable of generalizing to novel, unobserved tasks or languages with scant training data is a paramount research trajectory. Zero-shot and few-shot learning paradigms are likely to be pivotal in achieving this laudable objective.

Deployment and Practical Applicability:

Research efforts will be dedicated to the operationalization of end-to-end ASR systems in real-world contexts, necessitating considerations regarding computational efficiency, latency constraints, and adaptability to specific application domains.

These prospective avenues of inquiry underscore the unfolding trajectory of end-to-end ASR research, emblematic of a concerted effort to address salient real-world challenges and to harness the full potential of this technology.