How Do You Handle Code-Switching in Speech Data?
Why Understanding and Integrating Code-switching is Essential
Multilingualism is a way of life. In many regions, it is not uncommon to hear people switching between languages within the same conversation, sentence, or even word. This phenomenon, known as code-switching, reflects the dynamic reality of language use in diverse communities. For professionals working with speech data – including multilingual NLP engineers, language technology researchers, transcription project leads, localisation specialists, and sociolinguists – code-switching presents a complex set of challenges and valuable opportunities and is critical in speech data collection.
This outline explores how to handle code-switching in speech data, starting with its definition and linguistic structure, moving through the challenges it presents, and concluding with strategies for annotation, model training, and practical applications.
Defining Code-Switching
Code-switching is the practice of alternating between two or more languages within a single discourse. It is a common feature of speech in multilingual societies and can take different forms depending on how and where the language switch occurs.
Intra-utterance code-switching refers to the switching of languages within a single sentence or phrase. An example might be: “Let’s go to the winkel after work,” where the speaker switches from English to Afrikaans mid-sentence.
Inter-utterance code-switching happens when the switch occurs between sentences or utterances. For instance: “We’re meeting at six. Ek sal daar wees vroeg,” where the speaker shifts from English to Afrikaans after a sentence break.
These switches are not random. They follow sociolinguistic rules that reflect cultural identity, emotional expression, and conversational context. In countries like South Africa, where multiple languages co-exist in daily life, speakers frequently shift between English, isiXhosa, Afrikaans, isiZulu, and others depending on the setting, audience, and subject matter.
It is important to distinguish code-switching from borrowing. Borrowing involves integrating a foreign word into a primary language, often permanently. Code-switching, on the other hand, involves intentional alternation between languages within the structure of the discourse.
Recognising these distinctions allows speech technologists and linguists to more accurately process mixed-language audio and design systems that mirror how people actually speak in the real world.
Challenges Posed by Code-Switching
While code-switching is natural and often seamless for human speakers, it creates a number of challenges for those working with speech technology and transcription.
The first major challenge is confusion for automatic speech recognition (ASR) systems. Most ASR models are trained using monolingual datasets. When a speaker shifts languages, the model often fails to adjust, leading to misrecognition. For example, an English ASR model might attempt to transcribe isiXhosa words using English pronunciation rules, resulting in gibberish or incorrect output.
Another challenge lies in the transcription process itself. Transcribing code-switched speech requires fluency in all the languages being used. Transcribers must carefully identify where one language ends and another begins, even when the change is brief or ambiguous. For instance, short expressions like “Ja, I know” could be interpreted differently depending on the linguistic background of the speaker. Identifying whether “Ja” is Afrikaans, isiZulu, or even a habitual utterance in English requires both context and cultural familiarity.
Transcription complexity is further increased by the need to preserve speaker tone and the exact wording, particularly in informal speech that includes slang, contractions, or local expressions. This increases the cognitive load on transcribers, raises the likelihood of inconsistency, and extends the time required for accurate transcription.
Language identification is another persistent difficulty. Traditional language identification systems require long samples to make accurate determinations. Code-switched audio often contains very short segments in each language, making it difficult for these systems to keep up. In multilingual environments where tonal languages are common, such as isiZulu and isiXhosa, even human transcribers can struggle to distinguish language boundaries when speech is fast or heavily accented.
Without accurate language identification, downstream applications – including transcription, sentiment analysis, and real-time response systems – become unreliable, potentially compromising the integrity of the data and the systems relying on it.
Annotation Strategies for Mixed Speech
Accurate annotation is essential for working effectively with code-switched speech. Poorly labelled data leads to poorly performing models. The following best practices can help ensure high-quality annotation of mixed-language audio.
Firstly, every segment of speech should be tagged with its respective language. This includes even short words or phrases within a sentence. Language tags, such as ‘[en]’ for English or ‘[af]’ for Afrikaans, can be inserted directly before the relevant utterances. Accurate timestamping is also important so that each language segment is clearly located within the audio.
When speakers alternate between languages within the same sentence, annotations should reflect those precise boundaries. Language tags must be applied consistently and in a structured format. This ensures that any system using the data can clearly differentiate between languages and understand how they interact within the utterance.
Secondly, speaker identification is important. In multilingual dialogues, different speakers often use different dominant languages. Each speaker should be assigned a unique identifier, and the dataset should indicate which speaker is speaking at each point in the audio. Where applicable, noting a speaker’s dominant or preferred language can further support model training and evaluation.
Transliteration is also a consideration when working with languages that use different scripts. For instance, Arabic, Hindi, or Mandarin Chinese words may need to be transliterated into the Latin script used by the transcription platform. This process should follow a consistent standard, such as ISO transliteration, and should be documented in the project’s annotation guidelines.
Including metadata about the speaker’s background, region, age, and setting of the conversation can offer further insights into the code-switching patterns. These data points are especially useful for sociolinguistic analysis and for building systems tailored to specific populations.
Project leads should always provide a detailed style guide that outlines how to treat things like hesitations, repetitions, background noise, and informal language. The more comprehensive the guidelines, the more consistent and valuable the final annotations will be.
Model Training Approaches
Training models to handle code-switched speech requires approaches that reflect the complexity of multilingual communication. There is no one-size-fits-all solution, but several strategies are commonly used.
One approach is joint multilingual training. This involves training a single ASR model on data from multiple languages at the same time. This helps the model learn shared phonetic and syntactic features across languages. When exposed to sufficient data, such models are better able to handle natural language mixing. However, this method requires large quantities of annotated code-switched data, which is often difficult to source.
Another option is sequential or cascaded training. In this setup, a language identification model first determines which language is being spoken at a given time. Then, the corresponding monolingual ASR model is used to transcribe the segment. While this can work well in controlled environments, it introduces a dependency: if the language identification is wrong, the transcription will be too.
Language embedding techniques offer a more integrated approach. Here, the model is trained with additional input that tells it what language it should expect, either at the utterance or segment level. Embedding the language as a feature helps the model anticipate changes and adjust more accurately when code-switching occurs. Attention mechanisms can also be introduced to help the model learn which parts of the input are most relevant for decoding speech in multilingual contexts.
Transfer learning is another valuable technique, particularly in low-resource settings. A model trained on a large multilingual dataset – such as English and Spanish – can be fine-tuned using smaller datasets in other language pairs, like English and isiXhosa. This makes it possible to build functional models even when data is limited, which is often the case for many African and Indigenous languages.
When selecting a model training strategy, it is essential to consider the language pairing, domain of use, and availability of annotated data. Continuous testing on real-world code-switched samples is critical to ensure the model performs well outside the lab.
Applications and Use Cases
Accurately handling code-switching in speech data is not only a technical challenge but also a practical necessity across many domains.
In South Africa, code-switching between English and indigenous languages such as isiXhosa, isiZulu, or Afrikaans is widespread. This is common in schools, radio broadcasts, casual conversations, and even legal or medical consultations. Speech recognition systems that cannot accurately process these conversations risk excluding large segments of the population. ASR systems used in government services, educational platforms, and public messaging must be inclusive of multilingual realities.
Call centres and customer support services also face code-switching challenges. In many global service environments, agents adapt to customers by switching languages fluidly. Mixed-language audio needs to be accurately transcribed for compliance, quality assurance, and analytics purposes. Systems that can handle code-switching reduce errors, improve customer satisfaction, and support multilingual customer bases.
Code-switched speech also plays a role in language preservation. In communities where languages are shifting or at risk of extinction, analysing real-life speech patterns – including mixing – can help linguists understand language evolution. Such data offers insights into how younger generations adapt traditional languages to modern contexts.
Voice assistants and AI-driven interfaces are increasingly being used in multilingual homes and workplaces. If these systems can’t handle code-switching, users must stick to a single language, reducing usability. Incorporating code-switching detection into voice interfaces makes technology more accessible and intuitive for users who live and speak in more than one language daily.
Finally, sociolinguists use code-switched data to explore identity, community norms, and how language reflects cultural blending. Transcriptions of multilingual conversations can reveal how people express emotion, build rapport, or navigate complex social environments through their language choices.
All of these applications show that mastering code-switching is about more than linguistic theory. It’s about building technologies that serve real people, in real conversations, in the way they naturally speak.
Final Thoughts on Code-Switching in Speech Data
Handling code-switching in speech data requires a comprehensive approach. From careful annotation and smart training strategies to real-world applications, the goal is to reflect the authentic ways people speak across languages. By investing in accurate, inclusive solutions, we not only improve our technologies – we ensure they represent the diverse voices that shape our global society.
Whether you’re building AI for a multilingual city or transcribing interviews in a rural community, understanding and integrating code-switching is essential. As the world grows more connected and linguistically diverse, the ability to manage mixed-language audio is not a luxury – it’s a standard for inclusive and effective language technology.
Resources and Links
Wikipedia: Code-Switching – Provides a general introduction to the concept, including types and linguistic theories.
Way With Words: Speech Collection – Way With Words delivers high-quality multilingual datasets and mixed-language audio transcription for real-world applications. Their services support accurate speech data processing for ASR systems, language research, and AI model development.