How To Implement Speech Recognition [3 Ways & 7 Machine Learning Models]

by | Jan 31, 2024 | Natural Language Processing

What is Speech Recognition?

Speech recognition, also known as automatic speech recognition (ASR) or voice recognition, is a technology that converts spoken language into written text. The primary goal of speech recognition systems is to accurately and efficiently transcribe spoken words into a format that can be processed, stored, or used for various applications. This technology relies on sophisticated algorithms and machine learning techniques to interpret and understand human speech patterns.

The process of speech recognition involves several key steps:

  1. Acoustic Signal Processing: The input to a speech recognition system is an acoustic signal, the analogue waveform of the spoken words. This signal is captured by a microphone and converted into a digital format.
  2. Feature Extraction: The system extracts relevant features from the digital signal, such as spectral characteristics, pitch, and intensity. These features help in representing the unique aspects of the spoken language.
  3. Acoustic Model: An acoustic model is a statistical model trained to recognise speech’s acoustic features. Machine learning techniques, such as hidden Markov models (HMMs) or deep neural networks (DNNs), are commonly used to build these models.
  4. Language Model: Besides the acoustic model, a language model is employed to understand the context and structure of the spoken words. Language models help improve the accuracy of recognising words in their proper linguistic context.
  5. Decoding: The system uses the acoustic and language models to decode the input signal and generate a sequence of words or phrases that represent the most likely transcription of the spoken language.
the process of speech recognition

Speech recognition finds applications in various fields, including:

  • Personal Assistants: Virtual assistants like Siri, Google Assistant, and Alexa use speech recognition to understand and respond to user commands and queries.
  • Transcription Services: Speech-to-text systems are employed for converting spoken language into written text, facilitating tasks like transcription of meetings, interviews, or dictation.
  • Accessibility Features: Speech recognition enhances accessibility for individuals with disabilities, allowing them to control devices or communicate using their voice.
  • Customer Service: Automated voice systems in call centres often use speech recognition to understand and respond to customer inquiries.
  • Smart Homes: Voice-activated devices in smart homes utilise speech recognition for tasks such as controlling lights, thermostats, and other connected devices.
5 common applications of speech recognition

While speech recognition technology has advanced significantly in recent years, challenges such as handling different accents, dealing with background noise, and ensuring privacy and security remain areas of active research and development. Advances in deep learning and artificial intelligence have improved accuracy and expanded the range of applications for speech recognition systems.

How Does Speech Recognition Work?

Speech recognition is a complex process that involves multiple stages, from capturing spoken words to producing an accurate transcription. Understanding how speech recognition works requires delving into the intricacies of signal processing, feature extraction, and the application of advanced algorithms. Here’s a breakdown of the key components:

1. Acoustic Signal Processing

  • Capture the Analog Signal: The process begins with a microphone capturing the analogue signal of spoken words. This analogue signal is a continuous waveform representing the variations in air pressure caused by speech.
  • Analogue-to-Digital Conversion: The captured analogue signal is converted into a digital format through analog-to-digital conversion. This digital signal becomes the input for further processing.

2. Feature Extraction

  • Spectral Analysis: The digital signal undergoes spectral analysis to extract relevant features. This involves breaking down the signal into frequency components, revealing patterns representing speech sound characteristics.
  • Pitch and Intensity Analysis: Additional features, such as pitch (frequency of the speech) and intensity (loudness), are extracted to capture more nuances of the spoken language.

3. Acoustic Model

  • Statistical Models: An acoustic model is a statistical model trained on vast datasets of audio recordings. It learns to associate acoustic features with phonemes, the smallest sound units in a language.
  • Hidden Markov Models (HMMs) or Deep Neural Networks (DNNs): Traditional models like HMMs or more modern approaches like DNNs represent the probabilities of transitioning between different phonemes over time.

4. Language Model

  • Context and Structure: A language model complements the acoustic model by providing information about the structure and context of the spoken language. It helps the system distinguish between words that may sound similar but have different meanings.
  • Grammar and Syntax: The language model incorporates grammar and syntax rules, enabling the system to generate more accurate transcriptions based on the context of the spoken words.

5. Decoding

  • Matching Patterns: The acoustic and language models work together in decoding. The system matches the observed acoustic patterns with the learned models to identify the most probable sequence of phonemes and words.
  • Scoring and Ranking: Various hypotheses are scored based on their likelihood, and the system ranks them to determine the most probable transcription of the spoken language.

Understanding the intricacies of acoustic signal processing, feature extraction, and the utilisation of statistical and language models helps shed light on the remarkable capabilities of speech recognition systems. As technology advances, incorporating machine learning and deep learning techniques, the accuracy and versatility are expected to improve further, opening up new possibilities for human-computer interaction.

Top 7 Speech Recognition Models

Speech recognition models utilise various techniques and architectures to convert spoken language into written text. Over the years, different approaches have been employed, ranging from traditional statistical models to more advanced deep learning techniques. Here are some notable models and approaches:

1. Hidden Markov Models (HMMs)

  • Description: HMMs have been a traditional choice. They model speech as a sequence of hidden states, where each state corresponds to a phoneme or sub-word unit. Transition probabilities between states are learned from training data.
  • Application: HMMs have been used with Gaussian Mixture Models (GMMs) to represent acoustic models in speech recognition systems.

2. Deep Neural Networks (DNNs)

  • Description: DNNs have become a prominent advancement in speech recognition. These models consist of multiple layers of artificial neural networks that learn hierarchical representations of input features. DNNs replace GMMs in acoustic modelling, leading to improved accuracy.
  • Application: DNN-based acoustic models are widely used in traditional Automatic Speech Recognition (ASR) systems and modern deep learning-based ASR systems.

3. Convolutional Neural Networks (CNNs)

  • Description: CNNs, known for their success in image recognition, have been adapted for speech processing. They can capture hierarchical features in the spectrogram representations of speech signals.
  • Application: CNNs are employed in speech emotion recognition and speaker identification tasks.

4. Long Short-Term Memory (LSTM) Networks

  • Description: LSTMs are a recurrent neural network (RNN) type designed to address the vanishing gradient problem in traditional RNNs. LSTMs effectively model sequential dependencies in time-series data, making them suitable for speech recognition.
  • Application: LSTMs are used in various speech-related tasks, including ASR and language modelling.

5. Transformer Models

  • Description: Originally designed for natural language processing tasks, Transformer models have been adapted for speech recognition. They employ self-attention mechanisms to capture contextual information effectively.
  • Application: Transformer-based models, such as the DeepSpeech model, have demonstrated competitive performance in ASR tasks.

6. Connectionist Temporal Classification (CTC)

  • Description: CTC is a training criterion that enables end-to-end training of speech recognition systems without the need for explicit alignments between input and output sequences.
  • Application: CTC is often used with neural networks to train end-to-end ASR systems.

7. Hybrid Models

  • Description: Hybrid models combine the strengths of traditional HMM-based models and modern deep learning approaches. They often use neural networks for acoustic modelling and HMMs for modelling temporal dependencies.
  • Application: Some state-of-the-art ASR systems employ hybrid models for efficient and accuracy.

These models have contributed to significant advancements, enhancing accuracy, robustness, and the range of applications. The choice of model often depends on the specific requirements of the task, available data, and computational resources. Advances in deep learning continue to drive improvements.

How to Implement Speech Recognition in Python [3 Tools]

Several Python libraries and tools are available for implementing speech recognition in your projects. Here are some popular ones:

1. SpeechRecognition

The SpeechRecognition library provides a simple interface to various engines and APIs, making integrating with your Python applications easy.

  • Features:
    • Supports multiple engines, including Google Web Speech API, Sphinx, and
    • Allows for easy switching between different recognition engines.
    • Handles audio file input and microphone input.
import speech_recognition as sr

recognizer = sr.Recognizer()

with sr.Microphone() as source:
    print("Say something:")
    audio = recognizer.listen(source)

    text = recognizer.recognize_google(audio)
    print(f"You said: {text}")
except sr.UnknownValueError:
    print("Could not understand audio")
except sr.RequestError as e:
    print(f"Error with the speech recognition service; {e}")

2. Google Cloud Speech-to-Text API

Google Cloud Speech-to-Text API allows you to integrate Google’s powerful capabilities into your Python applications.

  • Features:
    • Supports multiple languages and audio formats.
    • Provides real-time and batch processing.
    • Offers high accuracy through machine learning models.
from import speech_v1p1beta1
from import enums

client = speech_v1p1beta1.SpeechClient()

audio_file = "path/to/audio/file.wav"
with open(audio_file, "rb") as audio_file:
    content =

audio = speech_v1p1beta1.RecognitionAudio(content=content)
config = speech_v1p1beta1.RecognitionConfig(

response = client.recognize(config=config, audio=audio)
for result in response.results:
    print("Transcript: {}".format(result.alternatives[0].transcript))

3. CMU Sphinx (PocketSphinx)

CMU Sphinx, also known as PocketSphinx, is a lightweight engine developed by Carnegie Mellon University.

  • Features:
    • Suitable for embedded systems and resource-constrained environments.
    • Provides both offline and online speech recognition.
from pocketsphinx import LiveSpeech

speech = LiveSpeech()

for phrase in speech:
    print("You said:", phrase)

These tools offer different features and capabilities, and the choice depends on your specific requirements, including accuracy, real-time processing, and integration with other services. Review the documentation for each tool to understand its features and usage in more detail.

8 Challenges in Speech Recognition

Despite the significant advancements in speech recognition technology, several challenges persist, impacting the accuracy and reliability of these systems. Overcoming these challenges is crucial for realising the full potential of speech recognition in various applications. Here are some of the key challenges:

  1. Accents, Dialects, and Language Variations:
    • Diverse Pronunciations: Speech recognition systems may struggle to interpret words pronounced with different accents or dialects accurately.
    • Language Variations: Variations in language, such as slang, colloquialisms, or regional expressions, pose challenges for recognition models.
  2. Background Noise and Environmental Factors:
    • Ambient Noise: Background noise from various sources, such as traffic, machinery, or conversations, can interfere with accurate speech recognition.
    • Acoustic Environment: Changes in acoustic environments, like echoes or reverberations, can affect audio input quality.
  3. Privacy and Security Concerns:
    • Sensitive Data Exposure: Speech recognition systems that process sensitive information raise concerns about data privacy and security.
    • Unauthorised Access: The risk of unauthorised access through voice commands, especially in voice-activated systems, is a notable security challenge.
  4. Continuous Improvement and Adaptation:
    • Dynamic Vocabulary: Adapting to evolving vocabularies, including new words, phrases, or industry-specific terminology, presents a continual challenge.
    • User Feedback Loop: Establishing effective feedback mechanisms for users to correct recognition errors and improve system performance over time is essential.
  5. Speaker Independence:
    • User-Specific Models: Speech recognition systems may struggle when users with different speaking styles or accents share the same device, requiring models to adapt to individual users.
    • Multi-Speaker Environments: Recognising speech with multiple speakers adds complexity significantly when speakers overlap.
  6. Lack of Context Understanding:
    • Contextual Ambiguity: Understanding the context in which words are spoken is challenging, as some words may have different meanings depending on the surrounding words or phrases.
    • Complex Syntax: Accurately capturing complex sentence structures and syntax remains challenging for many speech recognition systems.
  7. Adverse Conditions:
    • Emotional Speech: Recognising speech with emotional or expressive variations, such as anger, excitement, or hesitation, presents additional challenges.
    • Health Conditions: Speech recognition may be affected by conditions like speech disorders, making it challenging to interpret speech accurately.
  8. Multilingual Challenges:
    • Code-Switching: Multilingual environments with code-switching, where speakers switch between languages, can pose difficulties for recognition models.
    • Limited Language Support: Some languages may have limited resources for training robust speech recognition models.

Addressing these challenges involves a combination of advanced algorithmic improvements, robust training datasets encompassing diverse linguistic variations, and continuous user feedback mechanisms.

Advancements in Speech Recognition

The field has witnessed remarkable advancements driven by breakthroughs in machine learning, artificial intelligence, and signal processing. These innovations have improved the accuracy of transcriptions and expanded the range of applications. Here are key advancements contributing to the evolution:

  1. Deep Learning and Neural Networks:
    • DNNs in Acoustic Modeling: Integrating Deep Neural Networks (DNNs) into acoustic modelling has significantly enhanced the accuracy of speech recognition systems.
    • End-to-end Models: End-to-end models, which combine acoustic and language modelling into a single neural network, have demonstrated promising results, simplifying the architecture and training processes.
  2. Real-Time Transcription and Translation:
    • Streaming Recognition: Systems capable of streaming recognition in real-time enable immediate and accurate transcriptions during live interactions.
    • Multilingual Translations: Advancements in language modelling allow for real-time translation of spoken words into multiple languages.
  3. Multilingual and Cross-Language Recognition:
    • Language-Agnostic Models: Models that recognise speech across multiple languages without language-specific training sets have become more prevalent.
    • Code-Switching Support: Improved models better handle code-switching, where speakers seamlessly switch between languages within the same conversation.
  4. Integration with Emerging Technologies:
    • Augmented Reality (AR): Speech recognition is integrated with AR applications, allowing users to interact with digital environments using voice commands.
    • Virtual Assistants: Virtual assistants, such as Siri, Google Assistant, and Alexa, leverage advanced speech recognition for more natural and intuitive user interactions.
  5. Far-Field Speech Recognition:
    • Smart Home Devices: Far-field microphone arrays enable smart home devices to accurately recognise commands and queries from a distance, even in noisy environments.
    • Voice-Activated Systems: Improved far-field capabilities enhance voice-activated systems, providing a seamless user experience.
  6. Voice Biometrics and Identification:
    • Speaker Recognition: Voice biometrics have advanced, allowing for more accurate speaker identification and authentication.
    • Emotion Recognition: Speech recognition systems can now identify emotional cues in speech, providing insights into the speaker’s emotional state.
  7. Continuous Learning and Adaptation:
    • User-Specific Models: Systems that adapt to individual users over time, learning from user feedback and corrections, contribute to personalised and accurate recognition.
    • Incremental Learning: Techniques for incremental learning enable models to continuously improve without requiring retraining from scratch.
  8. Enhanced Noise Robustness:
    • Noise Reduction Techniques: Advanced noise reduction algorithms help speech recognition systems perform well in challenging acoustic environments.
    • Environmental Adaptation: Models can dynamically adapt to changes in the acoustic environment, ensuring robust performance in diverse settings.
  9. Open-Source Toolkits and Frameworks:
    • Accessible Development: Open-source toolkits like Mozilla DeepSpeech and Kaldi empower developers to build and customise speech recognition systems.
    • Community Contributions: The collaborative nature of open-source development accelerates innovation and the availability of diverse models.

These advancements collectively contribute to the growing capabilities of speech recognition technology, making it an integral part of our daily lives and a key enabler for various industries, including healthcare, customer service, and smart homes. As research continues, we can expect further breakthroughs, pushing the boundaries of what is possible with speech recognition.


The journey of speech recognition, from its early stages to the current state of advanced technologies, reflects a remarkable evolution in artificial intelligence and machine learning. The persistent efforts of researchers and developers have led to groundbreaking advancements, reshaping how we interact with technology and opening new frontiers of possibility.

Integrating Deep Neural Networks (DNNs), end-to-end models, and real-time transcription capabilities has significantly elevated the accuracy and efficiency. These advancements have made voice-controlled virtual assistants more responsive and facilitated seamless communication in diverse environments, from smart homes to professional settings.

The challenges that once posed obstacles to widespread adoption, such as accent variations, background noise, and security concerns, are being systematically addressed. Ongoing research aims to enhance the adaptability of models, making them more resilient to the complexities of real-world scenarios.

Moreover, the intersection with emerging technologies like augmented reality, virtual assistants, and voice biometrics promises a future where human-computer interaction becomes more natural, intuitive, and personalised. Far-field speech recognition, language-agnostic models, and continuous learning mechanisms contribute to a dynamic landscape where speech recognition systems evolve alongside user needs.

It is crucial to acknowledge the role of open-source frameworks and collaborative efforts within the developer community. Accessible tools and shared knowledge empower a more comprehensive range of individuals to contribute to the ongoing innovation in this field, fostering a collective momentum toward excellence.

Looking ahead, the trajectory of speech recognition holds exciting prospects. The pursuit of even greater accuracy, robustness, and adaptability continues, emphasising addressing the intricacies of multilingual environments, emotional speech recognition, and further refining user-specific models.

In conclusion, the story of speech recognition is one of continuous innovation, overcoming challenges, and transforming how we communicate with technology. As the journey unfolds, the collaborative spirit of the research community and the ever-expanding possibilities of advanced technologies promise a future where it becomes an even more integral and indispensable part of our connected world.

About the Author

Neri Van Otten

Neri Van Otten

Neri Van Otten is the founder of Spot Intelligence, a machine learning engineer with over 12 years of experience specialising in Natural Language Processing (NLP) and deep learning innovation. Dedicated to making your projects succeed.

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