An Overview of End-to-End Automatic Speech Recognition

September 5, 2022


End-to-End Automatic Speech Recognition(ASR) is a hot topic today as people are increasingly becoming informed of the ease of voice interaction because of the popularity of smart gadgets. Companies now are creating a wide range of products with precise transcribing skills at their core, thanks to advancements in voice recognition technology. Automatic Speech Recognition is a crucial component of many devices, including conversation intelligence platforms, personal assistants, and video and audio editing tools. 

In this blog, we will be briefing you on What is end-to-end automatic speech recognition? and a thorough review of Automatic Speech Recognition technology.

Table of Contents:

  1. What is an end-to-end automatic speech recognition system?
  2. How does end-to-end Automatic Speech Recognition work?
  3. Types of End-to-End Automatic Speech Recognition System.
  4. Characteristics of End-to-End Automatic Speech Recognition System.
  5. Advantages of End-to-End Automatic Speech Recognition System.
  6. Challenges of End-to-End Automatic Speech Recognition System.
  7. Conclusion.

What is an end-to-end automatic speech recognition system?

A system known as end-to-end immediately converts a series of input auditory data into a series of graphemes or words. End-to-End automatic speech recognition gives us a system that has been taught to optimise parameters linked to the evaluation measure we are interested in at the end.

Figure: End-to-end ASR Pipeline

The intricacy of conventional speech recognition is substantially simplified by end-to-end speech recognition. The neural network can automatically learn language or pronunciation information, thus there is no need to label the material explicitly.

How does end-to-end Automatic Speech Recognition work?

The goal of automatic speech recognition is to convert an audio input sequence X = {x1, · · · , xT} of length T, as a label sequence L = {l1, · · · , lN} of length N. 

The mathematical representation for ASR is:

Here V is the labels’ vocabulary, lu ∈ V is the label at position u in L. We use V to represent the collection of all label sequences formed by labels in V. The task of ASR is to find the most likely label sequence L given X.

The following components are found in most end-to-end speech recognition models: 

  1. Encoder: Converts voice input sequence into a feature sequence.
  2. Aligner: Achieves language and feature sequence alignment.
  3. Decoder: Decodes the final identification outcome. 

Figure: Functional Structure of end-to-end model

Please be aware that this division is not always there as end-to-end is a comprehensive structure and it is typically quite challenging to determine which component performs which sub-task when compared to a designed modular system. The end-to-end model achieves the direct mapping of auditory signals into label sequences without the use of well-thought-out intermediary stages. Additionally, there is no requirement for output posterior processing.

Types of End-to-End Automatic Speech Recognition System

Depending on how soft alignment is implemented, the end-to-end model can be classified into three groups

  1. Connectionist Temporal Classification (CTC)

End-to-end training of an acoustic model utilizing CTC as the loss function eliminates the requirement for prior data alignment and just requires the training of an input sequence and an output sequence. This eliminates the necessity for manual data alignment and labelling, and it also eliminates the need for additional post-processing for the likelihood of CTC's direct output sequence prediction. The CTC adds blank (which has no predicted value for this frame), where each prediction categorization corresponds to a spike in the entire speech and the other spots are regarded as blank. No matter how long a speech is, the CTC eventually produces a series of spikes.

  1. Attention model:

Encoder-Decoder-based Attention model first appeared in the context of the setting of neural machine translation. The primary usage of the Attention Mechanism is to address issues with the conventional RNN-based Seq2Seq model. The fundamental tenet of the Attention mechanism is that it overcomes the drawback of conventional encoder-decoder architecture, which depends on an internal vector of a constant length.

  1. RNN-transducer:

RNN-transducer end-to-end model lists every conceivable hard alignment before aggregating them to produce a soft alignment. However, RNN-transducer differs from CTC in terms of path design since it does not make independent assumptions about labels while enumerating hard alignments. as well as probability analysis.

Characteristics of End-to-End Automatic Speech Recognition System

Following are the four main characteristics of end-to-end automatic speech recognition systems.

  • Without further processing the input acoustic signature sequence, it immediately maps it to the text output sequence, achieving accurate transcription and enhancing recognition performance.

  • For collaborative training, several modules are combined into one network. Merging numerous modules has the advantage that fewer modules need to be designed in order to implement the mapping between different intermediate states. 

  • In order to get globally optimum outcomes, joint training enables the end-to-end model to employ a function that is highly relevant to the final assessment criteria as a global optimization objective.

  • The end-to-end model uses soft alignment. Each audio frame represents every conceivable state with a certain probability distribution, hence an explicit, forced correlation is not necessary.

Advantages of End-to-End Automatic Speech Recognition System

Certainly, there are many advantages of end-to-end Automatic speech recognition models over the traditional Hidden Markov model (HMM) and Gaussian Mixed model (GMM). The end-to-end approach has improved the results and accuracy more. Down below are the most important advantages of the end-to-end based model that makes it stand out from the rest of the models:

  • Using a single model, the end-to-end approach directly maps sounds to letters or words.
  • It substitutes the engineering process for learning and requires no domain expertise, the end-to-end model is easier to build and train.

Challenges of End-to-End Automatic Speech Recognition System

The constant drive toward human accuracy levels is one of the primary issues facing ASR today. Despite being substantially more precise than ever before, neither end-to-end ASR technique can guarantee 100% human accuracy. This is due to the complexity of our speech, which includes nuances in accent, slang, and pitch. Even the finest Deep Learning models require a lot of work to train and handle this vast tail of edge cases. Apart from this, below are the few challenges that end-to-end automatic speech recognition system faces.

  • End-to-end models are suited for low-latency online settings since they are monotonic and permit streaming decoding. However, their recognition performance is limited. These models can significantly boost identification performance, but they are unpredictable and have a large latency.

  • The whole end-to-end model's restricted coverage transcriptions of the training data serve as its sole source of language knowledge. Dealing with scenarios that have a lot of language variation becomes quite tough as a result. In order to preserve the end-to-end structure, the end-to-end model must enhance its ability to learn new languages.


In the coming days, there might be an explosion of applications utilizing ASR technology in their products to increase accessibility to audio and video data as it swiftly approaches human accuracy levels. Already, speech-to-text APIs are lowering costs, increasing accessibility, and improving the accuracy of ASR technology.

We may anticipate a deeper integration of Speech-to-Text technology into daily life as well as broader industry applications as the area of end-to-end ASR continues to develop. While end-to-end speech recognition based on end-to-end technology has currently produced impressive results, end-to-end speech recognition still requires language models to produce better results. In the future, it will be important to focus on how to further realize true end-to-end automatic speech recognition.

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CEOs must consider long-term cloud computing strategy and ensure that the organization is provided with the funding and resources for cloud adoption. The right communication is critical in cloud migration: employees should get these communications from C-suite executives in order to build confidence and guarantee adherence to governance requirements. Simply installing the cloud will not provide value for a company. Higher-level executives (particularly the CEO) must take the lead in the digital transformation path.

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