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BERT vs. RoBERTa: Which Model is Better for Analyzing Transcribed Speech?

by David Nguyen 11 min read
In this article

Ever wonder what’s actually happening behind the scenes when you upload a recording to your automated transcription platform? Modern speech-to-text systems can involve multiple AI components: automatic speech recognition converts audio into text, while language models and other NLP systems can analyze, classify, summarize, or refine that text.

BERT and RoBERTa are two influential models for understanding text. While neither directly transcribes audio, comparing them helps illustrate how improvements in language-model pretraining can affect downstream analysis of conversational transcripts. RoBERTa’s optimized training approach has produced stronger results than the original BERT on several major NLP benchmarks, including a sentiment-analysis study involving informal social-media language.

Key Takeaways

  • RoBERTa outperformed BERT by 2.85 percentage points in one 2025 sentiment-classification study, achieving 90.45% accuracy versus 87.60% on a dataset of 10,000 English-language mental-health-related tweets. The result demonstrates an advantage on that specific informal-text task, not transcription accuracy.
  • RoBERTa uses dynamic masking and a roughly 50K byte-level BPE vocabulary versus BERT’s roughly 30K-token vocabulary, alongside several other changes to the original BERT pretraining procedure.
  • The language model used for downstream text analysis should not be confused with the automatic speech recognition model that converts audio into text.
  • RoBERTa has demonstrated stronger performance than the original BERT on several major NLP benchmarks and on the cited informal-text sentiment task, but those results do not establish that a RoBERTa-based system will produce more accurate transcripts.
  • Sonix offers built-in AI analysis tools, including sentiment analysis, automated summaries, thematic analysis, and topic detection.
  • Comprehensive AI analysis features indicate strong downstream NLP capabilities, although they do not by themselves reveal which model powers a platform’s transcription engine.
  • Sonix supports automated transcription across 54+ languages, enabling multilingual workflows for teams processing diverse spoken content.
  • Sonix maintains enterprise-grade security controls and is SOC 2 Type II certified.

Understanding Large Language Models in Speech Recognition

Language models have changed what can be done with transcripts after speech is converted into text. But it’s important to distinguish them from automatic speech recognition: ASR processes an audio signal to produce a transcript, while text-focused language models such as BERT and RoBERTa process the resulting text.

Think about how you understand someone mid-sentence. You’re not just considering individual words; you’re interpreting each word based on its context. That’s essentially what models such as BERT and RoBERTa do with text, using transformer architecture to process words based on surrounding context. BERT, for example, was specifically designed to learn representations that jointly condition on left and right textual context.

Why this matters for transcript analysis:

  • Helps distinguish ambiguous words based on sentence context
  • Supports named entity recognition for people, organizations, and other entities
  • Enables sentiment and topic classification
  • Helps analyze informal or conversational text
  • Supports downstream summarization and information extraction when combined with appropriate models and systems

Speaker overlap, accents, audio noise, and speaker separation, by contrast, are primarily challenges for the speech-recognition and diarization portions of a transcription system.

BERT: The Foundation That Changed Everything

BERT (Bidirectional Encoder Representations from Transformers) arrived in 2018 and became one of the most influential models in modern natural language processing. Google’s creation introduced deep bidirectional pretraining, allowing its representations to account for both left and right textual context.

How BERT works:

BERT uses a technique called masked language modeling, where selected tokens are hidden or altered during training, forcing the model to predict them based on surrounding context. This bidirectional approach represented a major advance over language-representation approaches that processed context in only one direction.

BERT’s key specifications:

  • Trained primarily on BooksCorpus and English Wikipedia
  • Uses a roughly 30,000-token vocabulary
  • Uses a masking approach generated during preprocessing in the original implementation
  • Includes a Next Sentence Prediction (NSP) pretraining task

These characteristics are documented in the original BERT work and subsequent analysis of its pretraining procedure.

Where BERT excels:

  • Text classification tasks
  • Named entity recognition
  • Question answering systems
  • General language understanding tasks

BERT’s characteristics for conversational text:

Despite its groundbreaking nature, BERT’s original pretraining setup differs from later models such as RoBERTa. Its smaller pretraining corpus, masking procedure, vocabulary, and NSP objective all became areas of subsequent experimentation.

RoBERTa: The Optimized Evolution

Facebook AI, now Meta, released RoBERTa (Robustly Optimized BERT Pretraining Approach) in 2019, retaining BERT’s underlying architecture while changing important parts of the pretraining procedure. Its authors reported stronger results than the original BERT on major benchmark suites including GLUE, RACE, and SQuAD.

What makes RoBERTa different:

Training Data:

  • BERT: approximately 16GB in the comparison described by the RoBERTa researchers
  • RoBERTa: more than 160GB across five English-language corpora in its expanded training setup

Masking Approach:

  • BERT: Static/preprocessed masking in the original implementation
  • RoBERTa: Dynamic masking

Vocabulary Size:

  • BERT: Approximately 30K
  • RoBERTa: Approximately 50K byte-level BPE

NSP Task:

  • BERT: Included
  • RoBERTa: Removed

Training Duration:

  • BERT: Original training procedure
  • RoBERTa: Additional experimentation with substantially longer pretraining

RoBERTa’s pretraining advantages:

  • Dynamic masking: Instead of relying on masking patterns generated during preprocessing, RoBERTa generates a masking pattern each time a sequence is fed to the model. In the researchers’ controlled experiments, dynamic masking performed comparably or slightly better than static masking across the evaluated tasks.
  • Larger, byte-level vocabulary: The 50K byte-level BPE system can encode arbitrary input text without introducing unknown tokens. The RoBERTa researchers viewed this universal encoding property as beneficial, although their early experiments found only small performance differences between encoding approaches.
  • Removed NSP task: RoBERTa eliminated the Next Sentence Prediction loss as part of its optimized pretraining procedure. The researchers found that alternative training formats without NSP could match or improve performance on several evaluated text benchmarks.
  • More training data: RoBERTa’s expanded training used more than 160GB of text across several corpora, exposing the model to substantially more and more diverse textual material than the original BERT setup.

The Performance Gap: What the Numbers Actually Show

A 2025 comparative study provides a useful example of the difference between the models on one informal-text task. Researchers compared BERT and RoBERTa for sentiment classification using 10,000 English-language tweets related to mental health. RoBERTa achieved 90.45% accuracy, 89.78% precision, and 91.02% recall. BERT achieved 87.60% accuracy, 86.12% precision, and 85.37% recall.

The researchers attributed RoBERTa’s stronger results partly to its larger pretraining corpus and removal of NSP, describing it as better able to handle informal language and emotional expressions in the social-media dataset.

However, the distinction is important: this was a sentiment-classification experiment on written tweets, not a transcription experiment. It did not measure audio recognition, word error rate, speaker diarization, or performance on recorded conversations. The researchers also noted that their dataset covered only 10,000 English-language tweets and that generalizability beyond that setting was limited.

Potential advantages for analyzing conversational transcripts:

  • Conversational language: Models trained on broad textual datasets may be useful for downstream analysis of contractions, fragments, slang, and informal expressions.
  • Emotional content: Fine-tuned text models such as RoBERTa can perform sentiment and emotion-classification tasks on transcript text.
  • Context disambiguation: Bidirectional contextual representations can help distinguish meanings in ambiguous text.
  • Entity and topic analysis: Advanced NLP systems can identify entities, topics, themes, and other patterns after speech has been transcribed.

These are advantages for text analysis, not evidence that RoBERTa itself recognizes audio more accurately.

What This Means for Choosing Transcription Tools

Most users don’t need to select a transcription platform based on whether it uses BERT, RoBERTa, or another named architecture. A modern service may use different specialized models for speech recognition, speaker processing, sentiment, summarization, translation, and other tasks.

Instead, evaluate the results and capabilities that matter to your workflow:

Signs of sophisticated transcript analysis:

  • Built-in sentiment analysis
  • Automated summaries and topic extraction
  • Multi-transcript analysis capabilities
  • Custom prompts or structured analysis
  • Entity and theme detection
  • Search and analysis across collections of transcripts

Important transcription capabilities to evaluate separately:

  • Accuracy on your actual recordings
  • Performance with accents and background noise
  • Speaker identification
  • Handling of specialized terminology
  • Custom vocabulary support
  • Turnaround time

Platforms offering comprehensive AI analysis features such as thematic analysis, sentiment analysis, topic detection, entity extraction, automated summaries, and folder-level analysis clearly provide advanced downstream NLP functionality. Those features do not, however, identify the architecture powering the underlying speech-recognition engine.

How Advanced NLP Powers Professional Transcription Workflows

For businesses dealing with hours of recordings daily, combining reliable speech recognition with downstream language analysis can significantly change what teams can do with transcripts.

After transcription, advanced NLP tools can:

  • Analyze sentiment in transcript text
  • Identify themes, topics, and entities
  • Generate summaries
  • Answer questions about transcript content
  • Analyze patterns across collections of files

Those capabilities are distinct from the ASR system responsible for recognizing the spoken words themselves.

Sonix combines automated transcription with analysis tools inside the same platform. Sonix currently supports transcription in 54+ languages and offers AI features including automated summaries, sentiment analysis, thematic analysis, topic detection, and folder-level analysis across multiple files.

The practical workflow:

  • Upload your recording to a platform like Sonix
  • Receive a transcript with timestamps and speaker identification
  • Access AI-generated summaries highlighting key points
  • Review sentiment analysis of the transcript
  • Analyze multiple transcripts for broader patterns
  • Export your content for video editing, subtitles, or documentation

Sonix officially documents speaker detection, timestamps, AI analysis, and multiple transcript export options as part of its current feature set.

This comprehensive approach can turn a recording into both an editable transcript and material ready for further analysis.

Building the Right Transcription Workflow

Choosing transcription tools based on demonstrated capabilities rather than assumptions about their underlying architecture helps you make more meaningful comparisons. Here’s what to prioritize:

Essential capabilities for professional use:

  • Accuracy consistency: Test performance on the recordings and conditions that actually represent your work
  • Language breadth: Sonix supports transcription in 54+ languages and offers automated translation into 55+ languages on its current translation feature page
  • Security compliance: Sonix is SOC 2 Type II certified and documents additional security controls on its security page
  • Collaboration tools: Team features, shared workflows, and commenting can streamline review processes
  • Analysis integration: Built-in AI tools can reduce the need to move transcript data to separate analysis platforms

Questions to ask any transcription provider:

  • What accuracy do you achieve with content similar to mine?
  • Do you offer sentiment analysis or other transcript-analysis features?
  • How do you handle technical terminology and custom vocabulary?
  • What security certifications protect uploaded recordings?
  • Can I analyze patterns across multiple transcripts?

These answers are generally more useful than trying to infer which undisclosed model architecture a provider uses.

The Sonix Advantage: Your Foundation for LLM Analysis

Before a language model can analyze your spoken content as text, you need a transcript that accurately represents what was said. Errors in a transcript can affect downstream summaries, classification, search results, and other analyses.

Sonix combines automated transcription with editing and analysis capabilities designed to help teams review and work with their transcripts. Its automated-transcription feature currently supports speaker detection, timestamps, custom dictionaries for specialized vocabulary, and transcription across 54+ languages.

The Sonix approach to spoken-language workflows:

Why this matters for your LLM workflows:

Whether you’re analyzing transcripts with external language models or using Sonix’s built-in analysis capabilities, transcript quality affects the information available for downstream processing.

It’s also important not to assume a platform’s architecture from its feature list. Sentiment analysis, summarization, and theme extraction demonstrate downstream AI capabilities, but they do not reveal whether the transcription engine itself uses BERT, RoBERTa, or another architecture.

For teams serious about extracting insights from spoken content, the transcription platform isn’t just a utility. It supplies the text that downstream analysis depends on. Sonix combines that transcription workflow with built-in analysis tools for teams that want to move from recordings to searchable and analyzable content in one platform.

Frequently Asked Questions

Can BERT or RoBERTa directly transcribe audio files?

Neither BERT nor RoBERTa directly transcribes audio; both are text-focused language-representation models. Automatic speech recognition systems handle the conversion from audio signals into text. A transcription workflow may then apply separate language models or NLP systems for tasks such as classification, sentiment analysis, summarization, entity extraction, or other post-processing, but the exact architecture varies by provider.

Why don’t transcription companies disclose which models they use?

Transcription platforms do not always publish the details of every model or component in their technology stack, so a feature list generally cannot tell you whether a service uses BERT, RoBERTa, or another architecture. Sentiment analysis, automated summaries, and multi-transcript insights demonstrate downstream AI functionality, but they are not reliable evidence of the architecture used for speech recognition. When comparing platforms, focus on demonstrated transcription performance and documented features rather than attempting to infer an undisclosed model.

How much does model choice actually affect transcription accuracy?

The cited research does not answer that question. It found that RoBERTa achieved 90.45% accuracy versus BERT’s 87.60% on a sentiment-classification task involving 10,000 mental-health-related tweets, but classification accuracy is not the same metric as transcription accuracy or word error rate. The study therefore cannot be used to calculate how many transcription errors RoBERTa would prevent in an audio recording.

What features indicate a transcription platform uses advanced NLP?

Built-in AI analysis capabilities such as sentiment analysis, thematic analysis, topic detection, entity extraction, automated summaries, and multi-file analysis demonstrate that a platform has advanced text-analysis capabilities. They do not necessarily reveal which model powers its speech-recognition system, because transcription and downstream NLP may be handled by separate models. Sonix currently documents all of these analysis capabilities on its official AI Analysis page.

How can I test whether a transcription service handles spoken language well?

Upload content representative of your real workflow, including recordings with multiple speakers, specialized terminology, accents, or less-than-ideal recording conditions when those are typical for your use case. Evaluate word accuracy, speaker identification, terminology, timestamps, and the amount of manual correction required. Testing with your own recordings provides much stronger evidence about transcription quality than trying to infer performance from the name of an underlying language model.

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