NeuralDiarizer with the telephonic config mix speakers at the very beginning of shorter audio files (less than 2 minutes duration)
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**Describe the bug**

I am using NeuralDiarizer with the default diar_infer_telephonic.yaml settings (nemo version 1.21.0). I am using it to diarize the real-life phone call recordings.
I have experienced the same issue for almost any shorter audio file (less than 2 minutes duration) I have diarized: the first couple of utterances, pronounced by two different speakers, are merged into the same one, and labeled such that it was spoken by a single speaker. 
After the initial glitch, diarizer continues to work with the very precise predictions, so this issue is really only about the very first couple of sentences.

Any recommendation how to improve its precision for that particular problem? 

**Steps/Code to reproduce bug**

I am using NeuralDiarizer with the default diar_infer_telephonic.yaml settings file, with this addition:
```python
meta = {
        "audio_filepath": os.path.join(output_dir, "mono_file.wav"),
        "offset": 0,
        "duration": None,
        "label": "infer",
        "text": "-",
        "rttm_filepath": None,
        "uem_filepath": None,
    }
    with open(os.path.join(data_dir, "input_manifest.json"), "w") as fp:
        json.dump(meta, fp)
        fp.write("\n")

    pretrained_vad = "vad_multilingual_marblenet"
    pretrained_speaker_model = "titanet_large"
    config.num_workers = 0
    config.diarizer.manifest_filepath = os.path.join(data_dir, "input_manifest.json")
    config.diarizer.out_dir = (
        output_dir  # Directory to store intermediate files and prediction outputs
    )

    config.diarizer.speaker_embeddings.model_path = pretrained_speaker_model
    config.diarizer.oracle_vad = (
        False  # compute VAD provided with model_path to vad config
    )
    config.diarizer.clustering.parameters.oracle_num_speakers = False

    config.diarizer.clustering.parameters.enhanced_count_thres = 80
    config.diarizer.clustering.parameters.max_speaker_num = 2

    # Here, we use our in-house pretrained NeMo VAD model
    config.diarizer.vad.model_path = pretrained_vad
    config.diarizer.vad.parameters.onset = 0.8
    config.diarizer.vad.parameters.offset = 0.6
    config.diarizer.vad.parameters.pad_offset = -0.05
    config.diarizer.msdd_model.model_path = (
        "diar_msdd_telephonic"  # Telephonic speaker diarization model
    )
``` 
