Blog - Technical

Mar 21, 2023 | Read time 9 min

Accuracy Matters When Using GPT-4 and ChatGPT for Downstream Tasks

By combining the output of an automatic speech recognition (ASR) system with a large language model such as GPT-4 and ChatGPT, we can do many downstream tasks in a zero-shot way by including the transcript and task in the prompt.

Ana Olssen

Data Scientist

Speechmatics’ latest speech-to-text system, Ursa, can transcribe difficult audio with incredible accuracy, regardless of demographics. We have found that Ursa’s high transcription accuracy is crucial for high-quality downstream performance. Try the world’s most accurate transcription for yourself with just one click here!

At the most basic level, a large language model (LLM) is trained to predict the next word given the sequence of words that have come before. They learn to use that past context to build representations of how we use language at scale, reflected in the vast number of parameters they possess, the computational power required for their training, and the immense amount of training data. As a result, these models can perform tasks such as summarization in a zero-shot manner, meaning that they can generalize and solve problems without the need for any specific training examples pertaining to the given task[1]

ChatGPT[2] has taken the world by storm, producing a flurry of rap battles, poetry, and fitness plans with seamless dialogue with the user. ChatGPT is an LLM that has been fine-tuned using Reinforcement Learning with Human Feedback (RLHF)[3] and packaged in a chatbot user interface. More recently, OpenAI has released GPT4, an LLM with increased performance and the ability to accept images as input. We discuss the technical details and significance of GPT4 here

GPT4 and ChatGPT have shown remarkable performance in many areas of natural language processing; little analysis has been done on these models for tasks based on automatic speech recognition (ASR). We’ve found that they can gloss over some recognition errors and occasionally produce “better than input” answers to questions due to hallucinations based on the knowledge from training data, as in Figure 1. 

This is the ones the lungs the ions. Oh, yes sponges still made the book, Tiny Elsa sacs and around each one is the net a bad vessels and they take all children from the Elder that to breathe in.
Figure 1

Figure 1: ChatGPT hallucinates information about the lungs and attributes it to the speaker, even though this information is not contained in the input transcript. It was prompted with the transcript* from this video followed by “what does the speaker say about the lungs”. The relevant section begins at 4:12.

Generally, when a question is asked based on a specific transcript from ASR, you want the output to accurately reflect that input. We have found that Ursa’s high transcription accuracy is critical for consistent, high-quality downstream performance with no hallucinations. To demonstrate this, we use transcripts from Ursa and Google as input to GPT4 and ChatGPT and design prompts for five different tasks, as shown in Table 1. 

Task Prompt
Summarization <Transcript> Can you summarize this text?
Sentiment Analysis Identify the sentiment in this text: <Transcript>
Emotion Detection Identify the emotion in this text: <Transcript>
Named Entity Recognition Identify the named entities in this text: <Transcript>
Question Answering <Transcript> <question>?

Table 1: The five downstream tasks that are used to demonstrate the impact of ASR accuracy. The prompt design for each task is given where <Transcript> would be replaced with the output of the speech-to-text system and <question> replaced accordingly.


Summarization is condensing a long piece of text into a shorter one while maintaining the key points. We compared the quality of summarizations based on transcriptions from a technical machine learning video, and you can immediately see the impact of ASR accuracy on summarisation quality (Figure 2). ChatGPT perpetuates the error that Google makes in the key term, replacing “Lorentz transformation” with “Lawrence transformation”.

And if you write the inverse Lorentz transformation, what you do actually for writing the inverse Lorentz transformation, And if you write the inverse Lawrence transformation what you do actually for writing the universal orange transformation.
Image 2

Figure 2: Summarisation of a snippet from this technical video using ChatGPT on Ursa input (left) and Google input (right).

Sentiment Analysis

Sentiment analysis is identifying positive, negative, or neutral opinions. We trialled sentiment analysis on transcriptions from snippets in a food review video. The ASR accuracy has a transformational effect on the output quality for a negative and positive sentiment as shown in Figure 3 and 4, respectively. In both cases, the language models do not understand the input and explain in the replies that the text is “disjointed” and “jumbled”. 

I remember the ribs at these places like chicken George in Luton sells ribs. Our banging. Exactly. The sort of ribs you want when you have a load of chips dunking some gravy, some fried chicken wings and your ribs. I remember the bridge so these places like Chicken George in Luther sells ribs our banging exactly. So maybe if ribs you want me you have a bit like a chips donkey some gravy. Some brightness chicken wings and your ribs.
Image 3

Figure 3: Positive sentiment analysis from the food review video using GPT4 with Ursa input (left) and Google input (right). GPT4 is unable to identify the sentiment from the Google input.

It doesn't look appetizing to me. That looks like it's been sat there for days, bro. What's happened to it? Looks like it's going to be really hard. It doesn't it doesn't look advertising to me. That looks like it's been sat there for days bro. What's happened to it? Looks like it Is going to be really awesome.
Image 4

Figure 4: Negative sentiment analysis on a food review video using ChatGPT with Ursa input (left) and Google input (right). ChatGPT is unable to identify the sentiment from the Google input.

Emotion Detection

Emotion detection is a more nuanced analysis with the aim of identifying the human emotions expressed in a given text. Just like in the sentiment analysis example, the impact of ASR accuracy here is stark, with ChatGPT being able to extract the emotion for the Ursa transcription but not for Google, which contains more recognition errors (Figure 5).

Hi, it's me, Marissa. Today I'm going to share. Why do we have, like, a deep accent? And why is it difficult for us to pronounce the word knack? For example, my name, Marissa. Hi, it's me. Murita today. I'm going to share. Why do we help like a date extend and why you did the pickles for artists you bring out some wood? Not doing it. So my name. Marine Star
Image 5

Figure 5: Emotion detection of this clip by ChatGPT with Ursa input (left) and Google input (right)*. ChatGPT is unable to identify any emotions given the Google input.

Named Entity Recognition

Named Entity Recognition (NER) is the identification of proper nouns. These include names, such as John, London, or the United Nations, as well as times, dates, currencies, and numbers. ASR accuracy is particularly important for correct name generation, so Ursa-quality transcriptions lead to the correct proper nouns being extracted (Figure 6).  

I love using Dall-e to illustrate my my stories and I love speaking with Chatgpt where I mean, I have it on speed dial. I love using Dolly to illustrate. my my stories and I love speaking with Chad CPT were I mean I have it on speed dial.
Image 6

Figure 6: Named entity recognition (NER) from a snippet in this video using GPT4 based on Ursa input (left) and Google input (right). Incorrect spelling in the input transcript is carried through to the generated output.

Question Answering

Question answering (QA) can either be from a given text, known as closed QA, or from general knowledge, known as open QA. Figure 7 shows the difference in ASR accuracy once again produces meaningful differences, such as the company name “Coats and Gowns”, when asking the question “why did the caller want a refund?”. 

Thank you for calling coats and gowns. My name is Sam. How can I help you? Oh yes. I bought a coat from from you guys, Thank you for calling coaching and gowns. My name is Sam. How can I help you? Oh, yes. I bought a quote from from you guys,
Image 7

Figure 7: Question answering from this call centre video based on Ursa input (left) and Google input (right)*. The prompt included the question “why did the caller want a refund?“. With Google input, GPT4 is unable to identify the correct product purchased or the correct company name.

Accuracy Matters

Performing downstream tasks by adding ASR transcripts to a language model prompt is a very efficient way to condense, extract, or redact information. To demonstrate this, we fed Ursa and Google transcripts into GPT4 and ChatGPT. Results revealed that while these models can sometimes overlook transcript errors, the accuracy of the ASR transcript is crucial to ensure a high-quality output. Specifically, we showed how differences in ASR accuracy affected five downstream tasks: summarization, sentiment analysis, emotion detection, named entity recognition, and question answering. We demonstrated that Ursa produced transcripts with excellent accuracy, particularly on named entities, technical terminology, and difficult audio, resulting in more correct and detailed output. On the other hand, lower-accuracy transcripts caused errors that ranged from spelling mistakes to a complete inability to perform the requested task. 

These examples show what you can do with ASR transcripts and the importance of having the best ASR accuracy available when you do it. Next, we’ll take a more detailed look into Ursa's accuracy across different domains. Stay tuned to find out more!

Footnotes * The full transcript was provided in the prompt for these videos.

References [1] Brown, Tom, et al. "Language models are few-shot learners." Advances in neural information processing systems 33 (2020): 1877-1901.

[2] Open AI, "Introducing ChatGPT" OpenAI, 30 Nov, 2022. Accessed 13 Mar, 2023.

[3] Stiennon, Nisan, et al. "Learning to summarize with human feedback" Advances in Neural Information Processing Systems 33 (2020): 3008-3021.
Author Ana Olssen
Acknowledgements Benedetta Cevoli, John Hughes, Liam Steadman

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