Aug 29

How to leverage labelling to enhance accuracy in Automatic Speech Recognition 

In the realm of cutting-edge technology, where human-machine interaction is becoming more seamless and intuitive, Automatic Speech Recognition (ASR) stands as a pivotal innovation. ASR technology has made tremendous strides in converting spoken language into text, finding applications in voice assistants, transcription services, and more. However, the accuracy of ASR systems remains a critical factor in determining their real-world usability and effectiveness.  

This is where the art and science of labelling come into play. In this blog post, we delve into the fascinating world of how to leverage labelling to significantly enhance the accuracy of Automatic Speech Recognition. We'll uncover the core concepts, methodologies, and best practices that can turn speech data into a goldmine of insights for training ASR models that truly understand and accurately transcribe human speech. Whether you're a researcher, developer, or simply intrigued by the mechanics of ASR technology, get ready to embark on a journey that uncovers the secrets behind achieving exceptional ASR accuracy through strategic and effective labelling. 

What is Automatic Speech Recognition? 

Automatic Speech Recognition (ASR) is a transformative technology that converts spoken language into written text with remarkable accuracy. By leveraging advanced algorithms and machine learning, ASR systems enable computers to interpret and transcribe human speech, bridging the gap between verbal communication and digital data. ASR finds diverse applications in our daily lives, from virtual assistants like Siri and Alexa that understand voice commands, to transcription services that swiftly convert spoken content into written documents. 

a human talking to a AI robot with soundwave length symbol

The core of ASR lies in its ability to analyze audio input, identify individual words, and generate corresponding textual output. This process involves complex computations that match acoustic patterns to linguistic representations, allowing the system to decode spoken language effectively. ASR has revolutionized accessibility for individuals with hearing impairments, simplified data entry, and paved the way for voice-driven applications in sectors such as healthcare, customer service, and entertainment. 

Continual advancements in machine learning, neural networks, and data processing techniques continue to refine ASR accuracy, making it an indispensable tool for modern communication and interaction. 

What is the role of labelling in ASR? 

Labelling plays a pivotal role in Automatic Speech Recognition (ASR) due to its fundamental impact on training and refining ASR models. ASR systems rely on machine learning algorithms to recognize and transcribe spoken language accurately. Labelling involves the annotation of speech data, indicating the corresponding text transcription for each audio segment. There are countless reasons why labelling is critical to enhancing ASR accuracy, but we made a round up of the key points for you. 

  • Supervised learning: ASR models are trained using supervised learning, where they learn patterns by comparing input audio with their corresponding transcriptions. Accurate labelling provides the ground truth data needed for the model to learn and adapt effectively. 
  • Training data quality: The accuracy and reliability of the labelled data directly influence the quality of the trained model. Inaccurate or inconsistent labelling can lead to poor recognition performance and hinder model generalization. Read more: Ensuring quality in audio training data: key considerations for effective QA 
  • Feature learning: Labelling guides the model in learning relevant acoustic and linguistic features, helping it differentiate between different speech sounds, accents, and languages. These features are crucial for accurately transcribing diverse speech inputs. 
  • Language and context understanding: Labelling aids ASR models in understanding the context, semantics, and grammar of spoken language. It enables the system to grasp nuances, intonations, and variations specific to different speech situations. 
  • Error analysis and improvement: Accurate labelling allows for rigorous error analysis during model development and deployment. It helps identify common errors, misinterpretations, or areas where the model struggles, allowing developers to fine-tune and optimize the system. 
  • Adapting to domains: In specialized domains, like medical or legal transcription, labelling provides context-specific vocabulary and terminology. This ensures the ASR model accurately transcribes domain-specific jargon and terminology. 
  • Continuous learning: ASR models can be fine-tuned over time using new labelled data, adapting to evolving language trends and user behaviors. This process enhances long-term accuracy and relevance. 

Overall, labelling serves as the cornerstone of ASR technology by supplying accurate training data, aiding the model's ability to understand speech patterns, linguistic nuances, and context. It's the crucial bridge that transforms raw audio into a rich source of knowledge, enabling ASR systems to deliver accurate and meaningful transcriptions across various applications. 

Read more: Beginner’s guide to audio annotation and Annotation tools – market overview 

drawing of a human head shape with triangle icons

Challenges in ASR 

The main challenges of ASR relate to the types of speech being processed. There are 3 principal speech types, which are read, spontaneous and conversational. Typically these speech types are made available as datasets which are then used to train ASR algorithms. 

Read speech 

Read speech, where speakers recite prepared text, presents certain challenges in ASR due to its controlled and often unnatural nature. While read speech is commonly used in ASR research and benchmarking, it may not fully represent the complexities of spontaneous conversation. Read speech tends to lack the natural conversational flow, intonations, and emotional variability present in spontaneous speech.

ASR systems trained predominantly on read speech might struggle to accurately transcribe more dynamic and authentic spoken language. Speakers in read speech scenarios often enunciate words clearly and maintain consistent pacing. This reduced variability may lead to models that are less robust when confronted with real-world variations, such as accents, dialects, and speaking styles. Speakers in read speech situations might overemphasize words and sounds, which can distort phonetic patterns. ASR models trained on such data might have difficulty generalizing to naturally spoken content.

Read speech often involves specialized, technical, or formal vocabulary. ASR systems relying solely on this type of data might struggle to transcribe colloquial language, slang, or domain-specific jargon. Natural conversation contains contextual cues that help listeners understand the meaning behind words. Read speech typically lacks these cues, making it challenging for ASR models to accurately capture intended meanings.

Natural speech includes hesitations, false starts, repetitions, and fillers like "uh" and "um." Read speech usually lacks these disfluencies, making ASR models less adept at transcribing spontaneous speech patterns.

Prosody, including pitch, rhythm, and stress patterns, varies in natural speech due to emotions, intentions, and other factors. Read speech might lack this nuanced prosody, impacting the model's ability to interpret speaker intentions.

Read speech often lacks emotional variations, hindering ASR systems from accurately conveying the emotional context of spoken content.

If the training data for an ASR model is heavily skewed towards read speech in a particular domain (e.g., news broadcasts), the model might struggle when exposed to different domains with varying speaking styles, accents, and vocabulary. 

Spontaneous speech 

Spontaneous speech, characterized by its natural flow and dynamic variations, brings unique challenges for Automatic Speech Recognition (ASR) systems. Dealing with these challenges is essential to ensure accurate transcription and understanding of real-world spoken language.

Spontaneous speech encompasses a wide range of speaking styles, including formal, informal, conversational, and emotional. ASR models trained on a limited subset of these styles might struggle to accurately transcribe speech that deviates from the training data's style.

Natural conversation is peppered with hesitations, false starts, repetitions, and filler words ("uh," "um"). ASR systems need to recognize and handle these disfluencies appropriately to ensure accurate transcriptions.

In real-world scenarios, spontaneous speech often occurs in diverse acoustic environments with varying levels of background noise, making it challenging for ASR systems to distinguish between speech and noise, especially for low-quality recordings.

Conversations frequently involve multiple speakers talking simultaneously, overlapping, or interrupting each other. ASR models must be able to segment and transcribe these overlapping speech segments accurately.

Spontaneous speech reflects the diversity of language in terms of accents, dialects, and pronunciations. ASR systems trained on one accent might struggle to accurately transcribe speech from speakers with different accents, affecting overall recognition accuracy.

Prosody, encompassing pitch, rhythm, and intonation, conveys important information about emotions, intentions, and sentence structure in speech. Variability in prosody can make it challenging for ASR models to capture nuances accurately.

Spontaneous speech incorporates evolving language trends, neologisms, slang, and cultural references. ASR models trained on static vocabulary might struggle to transcribe newer terms accurately.

Natural conversation relies heavily on contextual cues and shared knowledge between interlocutors. ASR models need to grasp context to accurately disambiguate homophones and idiomatic expressions.

Speakers often use incomplete sentences or ellipsis to convey meaning. ASR models must infer missing information contextually to produce coherent transcriptions.

Spontaneous speech contains emotional nuances that influence pronunciation, pace, and tone. ASR systems must recognize and reflect these nuances to maintain accurate transcriptions with emotional context. 

Conversational speech 

Finally conversational data can also present multiple challenges within ASR.

Typically conversations involve multiple speakers taking turns to speak, sometimes overlapping or interrupting each other. ASR systems need to accurately segment and transcribe individual speakers' contributions while handling speaker changes seamlessly.

Unlike read speech, conversational speech lacks clear pauses or distinct segmentation between words. ASR models must be able to determine word boundaries and accurately segment the audio stream.

Conversations often include disfluencies like hesitations, false starts, and repetitions. ASR systems need to identify and appropriately handle these speech patterns to ensure accurate transcriptions.

Conversations can occur in noisy environments with various background sounds. ASR models must differentiate between speech and noise to provide accurate transcriptions.

Emotions play a significant role in conversational speech, affecting pitch, tone, and speech rate. ASR models must capture emotional nuances to maintain accurate transcriptions that convey the intended sentiments.

Conversations often rely on shared context and situational awareness between speakers. ASR systems need to comprehend context to accurately transcribe ambiguous or context-dependent words.

Conversations involve informal language, slang, and colloquialisms. ASR models trained on more formal speech data might struggle to accurately transcribe conversational language.

Gestures, facial expressions, and body language are integral to conversations, but they are not captured in audio data alone. ASR models must rely solely on auditory cues to transcribe spoken content.

In group conversations or noisy environments, overlapping conversations can occur in the background. ASR systems need to focus on the target conversation while filtering out irrelevant speech.

Speakers often use pronouns or refer to earlier parts of the conversation. ASR models must maintain context to accurately interpret and transcribe these indirect references.

Conversations within specialized domains might involve industry-specific jargon or technical terms. ASR models need exposure to these terms to transcribe accurately within those domains. 

Read more: Audio and speech segmentation: enhancing efficiency and accuracy with AI-assisted tools 

an Asian woman talking to a robot at a table

What is speaker diarization and why does it matter? 

Speaker diarization is a technique used in audio processing to automatically identify and segment different speakers within an audio recording. It involves breaking down the audio into segments based on changes in acoustic features, clustering segments with similar acoustic characteristics into groups representing individual speakers, assigning labels to these clusters to identify speakers, and sometimes tracking speaker identities over time in longer recordings.  

In the context of Automatic Speech Recognition (ASR), speaker diarization holds significance for multiple reasons. Firstly, it contributes to transcription accuracy by allowing ASR systems to tailor their recognition models to the unique characteristics of each speaker, thereby improving the accuracy of transcriptions. This is particularly valuable in scenarios involving multiple speakers or conversations.  

Moreover, speaker diarization enables ASR models to create personalized acoustic and language models for different speakers. By adapting the models to each speaker's specific accent, speaking style, and pronunciation, the ASR accuracy is further enhanced.  

Contextual understanding is another advantage. Recognizing different speakers aids ASR systems in correctly attributing spoken content to the appropriate speaker, which is especially important in conversations with multiple participants.  

Additionally, speaker diarization contributes to speaker-adaptive learning, allowing ASR models to adapt and improve over time by recognizing individual speakers and their patterns of speech. This results in improved performance across different speakers and situations. 

By providing insights into the roles, emotions, and intentions of different speakers, speaker diarization enriches the quality of transcribed content. This is beneficial not only for transcription accuracy but also for applications like audio indexing and content summarization, where knowing speaker identities helps in organizing, summarizing, and indexing audio content more effectively. In essence, speaker diarization is a crucial preprocessing step that enhances the accuracy, contextual understanding, and overall utility of ASR systems in dealing with real-world conversational data. 

Read more: What is speaker diarization?

What different methods are available for labelling data? 

Labeling data in Automatic Speech Recognition (ASR) involves annotating audio segments with their corresponding transcriptions, allowing ASR models to learn the mapping between spoken language and textual representation. There are several ways to label data in ASR, so let us explore the main methodologies with you here. 

  • Manual transcription: Human annotators listen to audio recordings and manually transcribe the spoken content into text. This method ensures high accuracy but can be time-consuming and expensive, especially for large datasets. 
  • Forced alignment: This technique involves aligning pre-existing transcriptions with the audio data using alignment tools. It can be more efficient than manual transcription, as it leverages existing text data, but still requires careful verification. 
  • Crowdsourcing: Outsourcing transcription tasks to a crowd of workers, often through platforms like Amazon Mechanical Turk, can help label large amounts of data quickly. However, maintaining quality control can be challenging. Read more: Where to get ML training data: StageZero vs. crowdsourcing marketplaces
  • Semi-supervised learning: Combining a smaller set of manually transcribed data with a larger set of automatically transcribed data can reduce the manual labeling effort while maintaining good model performance. 
  • Active learning: This approach involves training a model with a small labeled dataset and then selecting uncertain or challenging examples for manual annotation. This iterative process focuses human effort on the instances that would benefit the model the most. 
  • Transfer learning: Utilizing pre-trained language models (LMs) and fine-tuning them with domain-specific data can reduce the amount of required labeled data, as the LM's general language understanding is already established. 
  • Data augmentation: Generating new training samples by applying various transformations (e.g., speed changes, adding noise, altering pitch) to existing audio data can increase the diversity of the dataset and improve model generalization. 
  • Multilingual transcription: Training ASR models on data from multiple languages can help improve performance on languages with limited available labeled data due to shared acoustic and phonetic features. 
  • Weak supervision: Utilizing imperfect or noisy labels (e.g., automatic speech recognition transcriptions, subtitles, transcripts from the web) as a source of supervision can help scale up data labeling. 
  • Domain adaptation: Fine-tuning ASR models using a smaller amount of domain-specific data helps the model better understand the specific vocabulary and context of that domain. 
  • Language-specific pronunciation lexicons: For languages with complex pronunciation rules, providing explicit pronunciation guides for the vocabulary can help improve transcription accuracy. 

The choice of labeling method depends on factors such as available resources, data volume, annotation quality requirements, and the specific goals of the ASR project. In many cases, a combination of these methods may be used to effectively label data for ASR model training. 

What types of labels are typically used in ASR labelling? 

Typically you will want to label at least the following to enhance accuracy in your ASR projects. 

  • Word-level transcriptions: This is the most common and straightforward label type, where each word in the audio is transcribed and aligned with its corresponding position in the spoken content. 
  • Character-level transcriptions: Transcribing at the character level involves annotating each individual character, including letters, punctuation, and special symbols. It provides finer-grained information and helps handle different writing styles. 
  • Speaker labels: Identifying and labeling individual speakers in multi-speaker scenarios enables ASR models to distinguish between speakers and attribute spoken content to the correct speaker. 
  • Timestamps: Providing start and end timestamps for each spoken word or segment is valuable for aligning the transcription with the corresponding audio in time, enabling precise synchronization. 
  • Emotion labels: Annotating emotions expressed in the audio assists ASR models in capturing emotional nuances in transcriptions, which can be crucial for understanding context and sentiment. 

These label types provide essential information for training ASR models to accurately transcribe spoken language, while also capturing contextual, emotional, and speaker-specific aspects that enhance the richness of the transcribed content. 

data transcription illustration

Annotation software 

In order to ensure a smooth process when labelling data, specialists use annotation software. StageZero have integrated AI to their annotation software tool, to enhance accuracy even further. Users report that for every four hours of audio files, they save about one hour of transcription and labelling. So what exactly is this, and how does it help? 

First and foremost, the StageZero Annotation Tool plays a crucial role in streamlining the process of annotating audio data for ASR. It provides annotators with a user-friendly interface and various features that facilitate efficient and accurate labelling. Read more on the StageZero website and discover the principal features below: 

Audio visualization 

The StageZero Annotation Tool presents audio waveforms, spectrograms, or other visual representations of the audio data. This helps annotators identify speech segments, pauses, and changes in acoustic characteristics. 


The software enables annotators to segment the audio into meaningful units, such as words, phrases, or sentences, which are then associated with the corresponding textual transcriptions. 

Transcription interface 

The StageZero Annotation Tool provides a text entry interface alongside the audio visualization, allowing annotators to transcribe spoken content as they listen to the audio. 

StageZero audio annotation segmentation tool

Quick clicks 

Annotators can repeat common tasks at the click of a mouse button to speed up work by over 60%. Tasks like play, pause, rewind, and entering transcriptions can be time consuming so the StageZero developers wanted to ensure this was made easy for users. This improves annotator efficiency by minimizing the need to enter into complicated processes to complete simple and repetitive tasks. 

Speaker diarization  

As one of the few particularly advanced annotation tools on the market, the StageZero Annotation Tool includes speaker diarization capabilities, allowing annotators to assign different speakers to segments of the audio. This is especially valuable in multi-speaker recordings. 

Disfluencies and emotion marking  

The tool also includes options to label disfluencies (hesitations, repetitions) and emotional expressions, aiding in accurate transcription and context understanding. It is also possible to add custom requests to suit individual project requirements. 

Time stamping 

Annotators can insert time stamps at the start and end of each segment, which is useful for aligning transcriptions with the audio at a precise temporal level. 

Quality control 

Annotation software often includes quality control features, such as validation checks and double-checking mechanisms, to ensure the accuracy and consistency of annotations. 

Metadata management 

The StageZero Annotation Tool can store additional metadata, such as speaker IDs, recording conditions, or language information, alongside the annotations. 


Unlike the StageZero Annotation Tool, few annotation tools support collaborative annotation.  The StageZero Annotation Tool allows multiple annotators to work on the same project simultaneously and maintain version control. 

Export and import 

After annotating, the StageZero Annotation Tool facilitates exporting annotations in various formats (e.g., CSV, JSON) that can be used for training ASR models. Conversely, it can also import existing transcriptions for alignment or validation. 

Automated AI assistance 

As a unique player on the market, the StageZero Annotation Tool offers automatic transcription suggestions based on the audio, easing the annotation process by predicting potential transcriptions. 

Visualization of metadata 

The StageZero Annotation Tool allows users to visualize metadata like speaker labels, timestamps, or emotions, making it easier for annotators to understand and work with the data. 

By providing a dedicated and efficient environment for annotators, the StageZero Annotation Tool significantly speeds up the labelling process, maintains data quality, and ensures that ASR models are trained on accurate and well-structured annotated speech data. Ask the StageZero Team for your free trial license today. 

StageZero Technologies team working at their desks


In conclusion, the world of cutting-edge technology, where human-machine interaction is becoming increasingly intuitive, the transformative potential of Automatic Speech Recognition (ASR) shines through. ASR has revolutionized how we interact with devices, from voice assistants to transcription services. However, the quest for accuracy remains paramount. Enter the unsung hero: labelling. In our blog post, we embarked on a journey into the heart of ASR's accuracy enhancement. 

Labelling, the art of annotating audio data with corresponding transcriptions, holds the key to training ASR models that truly understand and transcribe spoken language. We delve into the myriad challenges that labelling overcomes, from handling noisy environments and diverse accents to capturing emotional nuances and conversational dynamics. Unraveling the diverse techniques in labelling, we explore manual transcription, crowdsourcing, and innovative strategies like active learning and transfer learning. 

As we navigated the intricacies of labelling, we uncovered its vital role in refining ASR systems for real-world scenarios. From training data quality and context understanding to speaker diarization and continuous learning, labelling propels ASR accuracy to new heights. Join us for more as we unveil the hidden power of labelling, and other key aspects ASR technology toward an era of exceptional accuracy and understanding in deciphering the spoken word. 

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