[ Sharing ]  Unveiling the World of Automatic Speech Recognition – Your Ultimate Guide!

Unveiling the World of Automatic Speech Recognition – Your Ultimate Guide!

  By : Leadtek AI Expert     66

Automatic Speech Recognition (ASR) or speech-to-text is a combination of processes and software that decodes human speech and converts it into digital text.

What is Automatic Speech Recognition (ASR)?

Automatic Speech Recognition (ASR) captures human speech and transforms it into readable text. ASR enables hands-free editing of text messages and provides a framework for machine understanding. The increasing searchability and operability of human language allow developers to access advanced analytics such as sentiment analysis. ASR serves as the first stage in the pipeline of conversational AI applications, facilitating communication between humans and machines using natural language.

Typical conversational AI applications use three subsystems to process and transcribe audio: understanding the presented question (extracting meaning), generating replies (text), and feeding back the replies to humans. These steps are achieved through the collaboration of multiple deep learning solutions.

Firstly, ASR processes raw audio signals and transcribes them into text. Secondly, Natural Language Processing (NLP) is used to extract meaning from the transcribed text (ASR output). Finally, Text-to-Speech (TTS) or speech synthesis is employed to artificially generate human speech from the text. Each step requires the construction and use of one or more deep learning models, making the optimization of this multi-step process highly complex.

Why Choose ASR?

The frequency of applications involving Speech Recognition and Conversational AI is increasing, whether in voice assistants, chatbots, or question-answering systems supporting customer self-service. Various industries, including finance and healthcare, adopt ASR or Conversational AI in their solutions.

The practical applications of speech-to-text are extensive:

  • "Busy" professionals like surgeons or pilots can record and issue commands during work.
  • Users can make voice requests or dictate messages when unable to use a keyboard or faced with danger while driving.
  • Voice-activated phone answering systems can handle complex requests without users navigating menus.
  • People with disabilities unable to use other input methods can interact with computers and other automated systems using voice.
  • Automatic transcription is faster and more cost-effective than manual transcription.
  • In most cases, ASR is faster than typing. An average person can speak about 150 words per minute but can type only around 40 words. Typing on a small and challenging smartphone keyboard can further slow down performance.
  • Speech-to-text is widely used in smartphones and desktop computers. It also finds applications in specialized fields such as medicine, law, and education. As its widespread adoption becomes mainstream, and it is extensively deployed in homes, cars, and office equipment, both academia and industry are intensifying research efforts in this field.

Working Principles of ASR

ASR is a challenging task in natural language that comprises a series of subtasks, including speech segmentation, acoustic modeling, and language modeling. It predicts (label sequences) based on noisy and unsegmented input data. Deep learning provides higher accuracy in identifying phonemes (basic sounds that create speech) and has replaced traditional ASR statistical methods like Hidden Markov Models and Gaussian Mixture Models. The introduction of deep learning Connectionist Temporal Classification (CTC) eliminates the need for pre-segmented data, allowing direct end-to-end training on the network for sequence labeling tasks like ASR.

The typical ASR CTC process includes the following steps:

Feature extraction: The first step is to extract useful audio features from the input audio, ignoring noise and other irrelevant information. Mel-frequency cepstral coefficients (MFCC) technology captures audio spectrum features in a spectrogram or Mel spectrogram.

Acoustic model: The spectrogram is passed to a deep learning-based acoustic model to predict character probabilities at each time step. During training, the acoustic model uses data containing hundreds of hours of audio and transcriptions of the target language (LibriSpeech ASR Corpus, The Wall Street Journal, TED-LIUM Corpus, and Google Audio set). The acoustic model outputs based on the pronunciation of words, so it may include repeated characters.

Decoding: Decoders and language models convert characters into word sequences based on context. These words can be further buffered into phrases and sentences, appropriately segmented, and then sent to the next stage.

Greedy (argmax): This is a simple strategy for the decoder. At each time step, it chooses the letter with the highest probability (from the time-sequenced Softmax output layer) without considering any semantic understanding of the content. Then, duplicate characters are removed or folded, and blank markers are discarded.

Language models can be used to add context, correcting errors in the acoustic model. A beam search decoder weights the relative probabilities of Softmax outputs with the probability of specific words appearing in context. It tries to combine what the acoustic model hears with the possible next word, determining the spoken content.

Using Deep Learning and GPU Acceleration for ASR

Innovative technologies like Connectionist Temporal Classification (CTC) integrate ASR directly into the field of deep learning. Popular deep learning models for ASR include Wav2letter, Deepspeech, LAS, and Jasper, recently released by NVIDIA, a popular toolkit using deep learning to develop speech applications. Kaldi is a C++ toolkit supporting traditional methods in addition to deep learning modules.

A GPU consisting of hundreds of cores can parallel process thousands of threads. Due to the neural network being built with many identical neurons, it inherently exhibits high parallelism. This parallelism naturally maps to the GPU, resulting in significantly improved computational speed compared to training relying solely on the CPU. For example, GPU-accelerated Kaldi solutions perform 3,500 times faster than real-time audio compared to a solution using only the CPU, which is 10 times faster. This performance makes the GPU the preferred platform for training deep learning models and performing inference.

Industry Applications


One of the challenges in healthcare is difficulty in accessibility. Waiting on hold when calling a doctor's office or contacting a claims representative promptly can be common issues. Using conversational AI to train chatbots is an emerging technology in healthcare, aiming to address the shortage of healthcare professionals and create communication channels with patients.

Financial Services

Conversational AI is building superior chatbots and AI assistants for financial services companies.


Chatbot technology is also commonly used in retail applications, accurately analyzing customer queries and generating replies or suggestions. This can streamline customer processes and improve store operational efficiency.

NVIDIA GPU-Accelerated Conversational AI Tools

Deploying conversational AI services may seem challenging, but NVIDIA now offers tools to simplify this process, including the Neural Modules (NeMo) and a new technology called NVIDIA Riva. To save time, the NVIDIA GPU Cloud (NGC) software center also provides pre-trained ASR models, training scripts, and performance results.

NVIDIA NeMo is a toolkit based on PyTorch designed for developing AI applications for conversational AI. In the development of modular deep neural networks, NeMo facilitates rapid experimentation by connecting modules, mixing and matching components. NeMo modules typically represent methods for data layers, encoders, decoders, language models, loss functions, or combined activation functions. NeMo utilizes reusable components for ASR, NLP, and TTS, making it easy to build complex neural network architectures and systems.

Furthermore, with the help of NVIDIA GPU Cloud (NGC), developers can access NeMo resources for conversational AI, such as pre-trained models, scripts for training or evaluation, and end-to-end NeMo applications, allowing experimentation with different algorithms and performing transfer learning on their datasets.

To facilitate the implementation and domain adaptation of the entire ASR process, NVIDIA has created domain-specific NeMo ASR applications. Developed with the assistance of NeMo, these applications support the training or fine-tuning of pre-trained ASR models (audio and language) using your own data. This allows for the step-by-step creation of higher-performing ASR models tailored to your specific data.

NVIDIA Riva is an application framework that provides multiple workflows for completing conversational AI tasks.

NVIDIA GPU-Accelerated End-to-End Data Science

Built on the CUDA foundation, the NVIDIA RAPIDS™ open-source software library suite enables you to fully execute end-to-end data science and analytics workflows on GPU while still using familiar interfaces like Pandas and Scikit-Learn APIs.

NVIDIA GPU-Accelerated Deep Learning Frameworks

GPU-accelerated deep learning frameworks bring flexibility to designing and training custom deep neural networks, providing programming interfaces for commonly used programming languages like Python and C/C++. Widely used deep learning frameworks such as MXNet, PyTorch, TensorFlow rely on the NVIDIA GPU acceleration library, delivering high-performance multi-GPU accelerated training.

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