In our interconnected world, clear communication across languages is important. Whether you're running a business, managing a team, or just trying to understand your customers, tools that help with transcribing, translating, summarizing, and analyzing conversations are incredibly useful. However, each of these tasks comes with its own set of challenges. A layered approach can help us get the best results, by tackling each step one at a time.
Layer 1: Transcription - Turning Speech into Text
The first step in understanding any conversation is transcription. This is where spoken words are converted into written text. Without a good transcription, everything else falls apart, so it’s crucial to get this right.
Several tools can help with transcription. Google Cloud, Whisper, and Sarvam are some of the best options. Google Cloud is known for its reliability and works well with a wide range of languages. Whisper, although open-source and less supported, is good for mixed-language audio. Sarvam is still in its early stages but shows promise, especially for local languages in India.
If you need to identify different speakers and add timestamps—known as speaker diarization—AWS (Amazon Web Services) is the go-to tool. It does a better job than most at distinguishing who is speaking, especially in conversations with multiple participants.
Layer 2: Translation - Making Communication Possible Across Languages
Once you have the conversation in text form, the next layer is translation. This step is vital when you're dealing with different languages, making sure everyone can understand the conversation.
There are several tools available for translation, including Google Cloud, Sarvam, iTranslate, and Quillbot. Which one you should use depends on the languages you’re dealing with. For popular languages like Hindi or widely spoken international languages, tools like Google Cloud, AWS, and Microsoft are strong choices. If you’re working with South Indian languages, Sarvam often does a better job.
However, translation isn’t perfect. For languages that don’t have as many samples, like Marathi or Odia, these tools can struggle a bit. Whisper models also perform well but lack customer support, which can be a problem if you run into issues.
Layer 3: Summarisation - Getting to the Point
After transcription and translation, you’re often left with a lot of text. That’s where summarization comes in. This layer helps condense large chunks of information into shorter, more digestible pieces, making it easier to get the gist without reading everything.
Tools like Cohere and OpenAI are great for this. They use advanced models to create concise summaries from large amounts of text. Depending on how much detail you want, you can adjust the length of the summary.
The cost of using these tools varies. OpenAI charges around $3 per million tokens for input and $1.5 per million tokens for output, while Cohere charges $2 per million tokens for input and $4 per million tokens for output. Both tools allow you to control the summary length, ensuring you get exactly what you need.
Layer 4: Sentiment and Topic Analysis - Understanding Mood and Meaning
The final layer involves analyzing the text to understand the mood (sentiment) and key themes (topics) of the conversation. This layer is crucial for figuring out how people feel and what they’re talking about, which is especially helpful for customer service or feedback.
Cohere and OpenAI are popular tools for identifying topics in conversations. They can categorize discussions into areas like complaints, suggestions, or queries. For sentiment analysis—understanding the tone of the conversation—**AWS** is a reliable choice, with Python libraries like TextBlob also being useful. These tools work best with English text, so translating the conversation to English first often gives better results.
There are also emerging tools like Sarvam, which are being developed specifically for local languages, though they’re still in the early stages.
Bringing It All Together: A Complete Solution
When you combine these layers—transcription, translation, summarization, and sentiment analysis—you get a powerful solution that can be used in many different ways. For example, you can create dashboards that show key metrics from conversations, like the number of calls received, the languages spoken, the general mood of the discussions, and how well agents are performing.
These insights can be visualized with tools like Tableau and Power BI, making it easier to keep track of what's happening, understand customer sentiment, and make informed decisions. The data, whether stored as text or audio, becomes a valuable resource for continuous improvement.
By treating transcription, translation, summarization, and sentiment analysis as separate but connected layers, you can build a complete solution that helps you understand and respond to conversations more effectively.
For more on this reach out to us on info@aghadvisors.com.
Comments