Ifeoluwa Oduwaiye
Apr 1, 2025
Introduction
When it comes to mainstream data analytics, tabular, text, and image data forms are often more sought-after than audio data. This is often because of the distinct differences in the way these types of data are analysed. Audio or speech data can be analysed to gain valuable insights such as sentiments, speaker identification, and transcription, and these particular insights drive mainstream accessibility, communication, and transcription engines.
Without audio analytics, we wouldn’t have voice assistants like Siri, Alexa, and Voice Search. Web conferencing applications like Microsoft Teams and Google Meet wouldn’t also have the real-time speech transcription engines that they have today.
Despite the vast number of use cases, audio analytics is often faced with challenges like diverse dialects, language switching, and background noise, there is still an opportunity for developers like yourself to overcome them.
In this blog, we will explore the world of audio analytics, taking a look at techniques like sentiment analysis, keyword detection, and speaker identification. We would also discuss valuable steps you can implement in building an AI-powered audio analytics pipeline, and how to get started with Spitch’s speech-to-text (STT) API.
The Value of Audio Data in Business and Technology
Businesses and companies generate all kinds of data; images from surveillance cameras, text from internal memorandum and email correspondence, tabular from transactions and audits, and audio from calls and meetings. However, most companies focus on utilizing tabular and text data alone for digital transformation and often end up sleeping on valuable insights that can be gotten from audio data.
According to McKinsey & Company, as of 2024, over 78% of companies had begun adopting AI into their processes, and out of these companies. In research done by DeveloperNation, only 20% of developers reported working with audio data.
Audio data is a rich source of insights because it carries not only the literal words but also the nuances of tone, emotion, and context. When customers share feedback over the phone, during meetings, or through recorded interactions, the audio captures their sentiments, stress levels, and engagement in a way that text alone cannot. This added dimension can help businesses gauge true customer satisfaction and uncover underlying issues that might otherwise go unnoticed.
When the audio analysis is done properly, the impact on operational efficiency, proactive customer support, and decision-making will be felt immediately and significantly. For example, automating the transcription of call center interactions, and meeting recordings, can help companies identify recurring issues, measure the performance of support agents, and detect areas for improvement.
This impact can be felt across a vast range of industries. Audio analytics can monitor agent performance and reduce customer churn in customer support. In healthcare, analyzing audio data can improve diagnosis accuracy and patient care by capturing subtle cues in patient speech. This list goes on and on to show the transformative potential of audio analytics in driving smarter, more efficient business outcomes.
Techniques for Extracting Insights from Speech Data
There are a number of techniques that can be used to extract insights into speech data. Some of them are:
Speech Transcription
One of the most popularly used techniques for mining insights from audio data is speech transcription. Speech transcription, also known as ASR (Automatic Speech Recognition) is the process of converting audio into text using audio manipulation and recognition technologies. This technique is popularly used in a wide range of applications to improve accessibility, automate manual transcription tasks like notes taking during meetings, transcription of interviews, and power voice assistants, and real-time captioning applications.
One of the earliest speech transcription technologies was Audrey, an Automatic Digit Recognition machine developed by Bell Laboratories in 1952, capable of recognizing spoken digits (0-9) with an accuracy of over 90%, but only for a specific speaker. IBM’s Shoebox came next in 1962 with an increased vocabulary of 16 English words.
Nowadays, we have speech transcription models with word error rates (WER) lower than 5%. Open AI’s Whisper and Google’s Gemini outperform most models today.
Sentiment Analysis
Sentiment analysis is another technique for analyzing audio data. By leveraging AI models, businesses can evaluate tone, emotion, and context within spoken language. This is particularly useful in B2C firms that always want to ensure customers are satisfied with their products.
By examining features such as pitch, intonation, and speech rate, these models can determine whether the speaker’s mood is positive, negative, or neutral. One of the major applications of this technique is in customer service, where understanding the emotional state of a caller can lead to more empathetic and effective responses. Additionally, in brand monitoring, sentiment analysis can help companies gauge public perception during product launches or crisis events, enabling them to tailor their messaging and strategies accordingly.
To build an audio sentiment analysis model, you can make use of CM-BERT (Cross-Modal BERT for Text-Audio Sentiment Analysis), MFCC (Mel-frequency Cepstral Coefficients), Prosodic Feature Analysis, and even LSTM (Long-Short Term Memory).
Source: CM-BERT
Keyword Detection
Keyword detection involves the extraction of significant words or phrases from audio data, which can then be used to identify key topics or recurring issues. NLP (Natural Language Processing) combined with acoustic analysis, can help filter out filler words and background noise to focus on the most relevant content in spoken speech.
This process informs marketing strategies by pinpointing trending topics or customer pain points, and it supports product development through insights gleaned from user feedback. For example, detecting keywords in customer calls can help a company refine its support scripts or even guide future product features.
Speaker Identification
Speaker identification uses machine learning algorithms to distinguish between different voices in a multi-speaker environment. By analyzing vocal characteristics and speech patterns, these systems assign unique identifiers to individual speakers, making it possible to attribute specific comments to the right person during group conversations.
Voice authentication systems that first appeared in movies like Mission Impossible are now a reality. They are used in online banking and advanced security devices, thanks to speaker identification technology. Speaker identification can be applied in call centres to power detailed customer interaction analytics and agent performance tracking systems.
Speaker identification can also be applied in forensic analysis and meeting transcriptions. Accurate speaker identification helps maintain clear records of who said what, enhancing accountability and data usability.
Speaker identification can be implemented using either traditional approaches like i-vector systems and x-vector models, or modern architectures like ECAPA-TDNN (Emphasized Channel Attention, Propagation, and Aggregation Time Delay Neural Network), transformer-based architectures and ResNet-based models.
Speech Summarization
Speech summarization uses ASR and NLP techniques to condense lengthy audio recordings into concise summaries that capture key points and insights. This process starts with transcribing the audio to text and then applies algorithms to identify the most relevant segments of the conversation.
This technique is invaluable in scenarios such as meeting recordings, where busy professionals need to quickly review highlights without listening to entire discussions. Industries like finance and legal services leverage this technique to rapidly extract critical information from extensive audio records, saving time and enhancing decision-making.
Building an AI-Powered Audio Analytics Pipeline
In the previous section, we analysed some techniques you can use to analyse audio data. In this section, we will be discussing the steps to approach toward building your own AI-powered audio analytics pipeline.
Step 1: Data Ingestion & Preprocessing
This is where you collect the actual audio data, whether it's recorded calls, meeting recordings, or any other source. The first task here is to ingest this data into your system. Then, preprocess the audio by cleaning it to improve the quality of the subsequent transcription. Watch out for background noise and uneven volume levels, and address them using the appropriate preprocessing techniques.
Step 2: Speech-to-Text Conversion
STT Conversion is a very important step in almost all audio analytics techniques. Once the ingested audio data have been preprocessed, the next step is to convert the speech into text using a robust speech-to-text (STT) engine. High-quality transcription is critical, as errors at this stage will affect the rest of the pipeline.
At this stage, you have the option to either build your own STT engine from scratch or leverage an existing solution. One excellent option to consider is Spitch’s STT API, which offers efficient, real-time audio transcription and is highly recommended. Spitch also offers support for African languages like Yoruba, Igbo, and Hausa.
Step 3: Downstream Analytics
Once the transcription process is done, you can extract your preferred insights using AI models. Some insights you can extract at this stage are sentiments, speaker identification, keywords, topics, tone, and so on.
In this step, you’ll want to take a look at your Software Requirements Document (SRD) to ensure that you are extracting all the right metrics and insights from your audio data. It is also important to pay attention to the data storage systems used here to avoid errors due to poor storage and avoidable data losses. These insights can be combined to build a comprehensive understanding of customer interactions, improving decision-making and operational efficiency.
Step 4: Integration and Visualization
Transforming raw audio data into actionable insights is the ultimate goal of any audio analytics pipeline. By applying techniques like sentiment analysis, keyword extraction, and speaker identification, you can uncover detailed information about customer interactions, employee communications, and operational workflows. However, these insights are only valuable if they can be easily interpreted and acted upon.
Therefore, it is important to integrate the outputs from your downstream analytics into a unified dashboard using tools like Tableau or Power BI. Finalizing your audio analytics pipeline with a centralized view not only accelerates decision-making but can also help your organization quickly adapt strategies based on generated insights.
How to get started with Spitch’s STT API
Spitch has developed Spitch transcription APIs which developers and businesses can leverage in building solutions inclusive of African languages. We currently have support for Yoruba, Hausa, Igbo and English, and are currently working on expanding to more African languages soon.
To get started with our services, head to our platform and sign up. All news users on Spitch get access to $1 worth of credits to try out our services. We have support for languages like Python, Javascript, PHP, Go, Java and cURL. You can get started by accessing our docs here. Additionally, you can check out our blog on building an AI-powered speech transcription app.
Concluding Remarks
Audio analytics is not without challenges. The major challenges are background noise, accent variability, and an acute scarcity of data for low-resource languages. These challenges are not without solutions as they can be overcome with the appropriate preprocessing technique.
In this article, we discussed key techniques for analysing audio data, how to design an audio analytics pipeline of your own, and how to make use of Spitch’s STT API for speech transcription. Building a proper audio analytics pipeline can help businesses harness the power of real-time transcription and analysis to drive smarter decision-making.
In summary, it is important to address the current challenges in audio analytics and anticipate future trends to stay ahead of the evolving landscape. If you're ready to transform your customer interactions and gain a competitive edge, explore Spitch’s innovative STT solutions and start integrating advanced audio analytics into your systems today.
